Author:
Diego Navarra EU Business School, Geneva, Switzerland
Digital Campus & Studio Navarra, UK

Search for other papers by Diego Navarra in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-5650-4994
Open access

Abstract

The objective of this paper is to review the academic literature and identify best practices on the integration of artificial intelligence and sustainable technologies in strategic renewable energy and Power-to-X projects globally. We reflect upon the way in which exemplary case studies can be used to foster a common shared view among different policy makers to highlight new ways through which energy efficiency and systemic improvements in the energy sector may be achieved while curbing carbon emissions and addressing climate change. The main risks, challenges and mitigations for integrating artificial intelligence and sustainable technologies in energy systems are also educed from both the academic literature as well as interviews with experts. Our findings indicate that while the integration of artificial intelligence and sustainable technologies can support energy efficiency and systemic improvements in the energy sector, there are several risks that were not previously identified in the literature. Critical areas of future development for academic research as well as opportunities for professional practice are presented.

Abstract

The objective of this paper is to review the academic literature and identify best practices on the integration of artificial intelligence and sustainable technologies in strategic renewable energy and Power-to-X projects globally. We reflect upon the way in which exemplary case studies can be used to foster a common shared view among different policy makers to highlight new ways through which energy efficiency and systemic improvements in the energy sector may be achieved while curbing carbon emissions and addressing climate change. The main risks, challenges and mitigations for integrating artificial intelligence and sustainable technologies in energy systems are also educed from both the academic literature as well as interviews with experts. Our findings indicate that while the integration of artificial intelligence and sustainable technologies can support energy efficiency and systemic improvements in the energy sector, there are several risks that were not previously identified in the literature. Critical areas of future development for academic research as well as opportunities for professional practice are presented.

1 Introduction

According to Anne-Sophie Corbeau, a global research scholar at the Center on Global Energy Policy at Columbia University's School of International and Public Affairs, there has been a 25% reduction in hydrogen generation in Europe to produce electricity in 2022. To counterbalance such reduction, also due to the planned decommissioning of nuclear power stations both in Germany and France, Europe is consuming more gas for electricity power generation (Columbia University 2022).

The natural gas and power generation sectors are inter-linked because about 25% of electricity generation in Europe comes from natural gas. According to a 2020 review of Power-to-X Demonstration projects in Europe (Wulf et al. 2020), the utilization of renewable electricity to produce hydrogen through the electrolysis of water is at the heart of most Power-to-X (PtX) concepts. Power-to-X (also known as P2X or PtX) describes the conversion of renewable electricity from wind, water or solar as primary energy into an energy carrier (“X”) (Bofinger 2022). This technology allows surplus production of electricity to be stored for later use while at the same time potentially minimising CO2 emissions. The contribution of gas generated from surplus renewable energy sources using Power-to-X technologies for electricity conversion, energy storage, and reconversion pathways can provide substantial input to fill the gap in the gas supplies that have been reduced due to ongoing geo-political events. Artificial intelligence (AI) and data driven decision making can be used to reduce risk, enhance organisations' adaptiveness to uncertain environments while increasing productivity and improving climate resilience in distributed energy systems (Navarra 2022a).

The utilisation of sustainable technologies in energy systems would support the optimisation of the production of renewable energy sources and reusable and recyclable resources (Stirling 2007; Levi-Jaksic et al. 2018; Rani et al. 2021). However, sustainable technologies can also play a vital role in other aspects of human lives and the achievement of Sustainable Development Goals, reduce the impact of climate change, devise innovative methods to create less pollution and waste (Shafiei – Abadi 2017; Stritch et al. 2018). Examples include transportation and urban infrastructure, acting as a preventive mechanism to dangerous technological failures, as well as to optimise effective consumption and production across the whole urban landscape (Navarra 2022b).

High expectations surround the integration of AI and sustainable technologies, however most of the ongoing strategic renewable energy and PtX projects are still at the laboratory scale (Barbraresi et al. 2022). Therefore, the goals of the article are to: 1) review the academic literature on the integration of artificial intelligence and sustainable technologies in strategic renewable energy and Power-to-X projects in the EU region and globally; 2) identify best practices related to the integration of artificial intelligence and sustainable technologies in the energy sector; 3) reflect upon how exemplary case studies can be used to foster a common shared view among different policy makers; 4) highlight new ways to achieve energy efficiency and systemic improvements in the energy sector while reducing carbon emissions and addressing climate change; 5) unveil the main risks, challenges and mitigation actions associated with the integration of AI and sustainable technologies in energy systems; and 6) identify critical areas of future development for academic research and for professional practice.

The detailed application of AI and sustainable technologies to the analysis of energy efficiency potential, smart charging, electricity market dynamics and modelling, energy trading, electricity load forecasting and real time grid monitoring are beyond the scope of the research.

This paper will address the following research questions and propositions:

Research question

What role do AI and sustainable technologies play in strategic renewable energy and Power-to-X projects?

Proposition

The integration of AI and sustainable technologies in strategic renewable energy and Power-to-X projects can support energy efficiency and systemic improvements in the energy sector in the EU region and globally.

Research question

In what way can best practices and exemplary case studies be used to foster a common shared view among different policy makers?

Proposition

Best practices and exemplary case studies can be used to foster a common shared view among different policy makers on how to counteract high energy prices while at the same time curbing carbon emissions and addressing climate change.

Research question

What are the main risks and challenges associated with integrating AI in energy systems and how can these risks and challenges be addressed?

Proposition

The main risks and challenges associated with integrating AI in energy systems include energy transitions, assumptions about human behaviour, lack of detailed data, and issues around cybersecurity.

The rest of the paper is structured as follows: first we present a systematic review focussed on the scientific evidence available in relation to strategic renewable energy and Power-to-X projects, AI, and sustainable technologies. Next, we discuss exemplary case studies grounding the perceived potential of the integration of AI, Power-to-X projects, and sustainable technologies into concrete practices and applications. Conclusions and recommendations for future research and professional practice follow in consideration of future developments and the main risks and challenges identified for integrating AI and smart sustainable technologies in distributed energy systems.

2 AI, Power-to-X, and energy systems: a systematic literature review

For this paper, we carried out a systematic literature review using specific keywords and phrases in our search within reputable scientific databases available on the web, including: University Library Databases, Emerald Insight, Science Direct, IEEE Xplore, ResearchGate, Academia.edu and Google Scholar. Moreover, relevant books on topics relating to the focus of the research projects as well as professionals and academics working in the filed have also been discovered via an Amazon.com and a LinkedIn search.

We used the search terms “AI”+ “Power-to-X”+“sustainability”+“digital technologies”+“risk”+“challenges” several times between June and December 2022 to identify relevant contributions to be included in the review. We followed a multi-stage process, involving planning the review via the identification of possible data sources and focusing only on articles published between 2014 and 2022, due to the novelty of the topic at hand. Initial findings involved a detailed analysis of the papers identified across the several databases, in order to appraise their quality and relevance. 280 unique works and case studies were analysed overall. Of these, 75 were most closely related to the synthesis presented as part of the paper's detailed analysis and discussion.

The following categories were identified in relation to the integration of AI, smart sustainable technologies and controls as a way to achieve energy efficiency globally: electricity production, power delivery, electric distribution networks, energy storage, energy saving, new energy materials and devices, energy efficiency and nanotechnology, energy policy and economics, future energy systems with high shares of renewable energies.

According to Wulf et al. (2020) as of June 2020, a total of 220 PtX research and demonstration projects in Europe have either been realized, completed, or were being planned with France and Germany undertaking the biggest efforts to develop PtX technologies compared to other European countries. The same research indicates that although interest in PtX projects has been growing constantly, at least until 2030, the only new countries to launch PtX projects are Hungary and Slovenia (Wulf et al. 2020).

In October 2022, the European Commission published a document outlining plans to digitalise the energy sector and to make it smarter and more interactive than it is today (Eur-Lex 2022). According to the Commission's publication, the European Green Deal and the Digital Decade Policy Programme 2030 for Europe go hand-in-hand as a twin transition for the digitisation of the energy system. The European Commission also plans to develop an experimental platform for blockchain-based energy trading, as well as actions to restrict energy use for crypto mining during the current crisis. The plan mainly involves distributed renewable energy projects which allow households and businesses greater control to provide solar-generated power to the grid or a local microgrid, with compensations recorded on blockchain.

The blockchain has the potential to verify the authenticity of financial transactions and traded goods by providing an electronic ledger for auditing, however it is not the only potential ‘sustainable’ technology that may be employed. For instance, augmented reality can enhance warehouse and plant workers' efficiency providing precise data on the actions that should be taken, from repairing general equipment to the point of any line requiring intervention. Likewise, 3D printing makes it possible to obtain electronic drawings and to print (rather than order) spare parts. Finally, recent development in telecommunications such as 5G facilitate ever larger amounts of data sharing at greater speeds between connected devices than ever before. Such novel practices, directly and indirectly, can also stimulate cleaner energy practices that can combat climate change and enhance resilience of energy systems.

Nevertheless, according to Eur-Lex (2022): ‘the key enabler for a digitalised energy system is the availability of, access to, and sharing of energy-related data based on seamless and secure data transfers among trusted parties’. The latter may represent a crucial yet narrow focus on the diversity of sustainable technologies which may (when combined) have the greatest impact in turning industry networks into digital ecosystems as envisaged by the plan. However, this plan should also incorporate sustainable technological developments that expand the frontiers of legacy organisational structures and systems. To be sure, what can be achieved with ‘energy-related data based on seamless and secure data’ are possible thanks to three technologies: AI, sensors and the Internet of Things (IoT) spanning power generation at different scales, including control rooms, turbines, speed pumps, motors, distributed battery energy storage, smart meters, etc.

According to Subramaniam (2022) sensors allow firms to collect real-time interactive data from assets, products, and customers. IoT allows various physical assets to be connected to the internet through protocols such as Wi-Fi, Bluetooth, or similar. Finally, AI is a term that encompasses many different technologies, such as statistical machine learning, neural networks, natural language processing, or robotic process automation. We agree with Subramaniam (2022) in relation to the meaning of AI in the context of the extant research: namely a technology that recognizes patterns in large amounts of data that humans may miss. However, AI also enables the possibility to infer predictive patterns based on the data analysis, thus supporting decision-making. The data collected and processed from sensors, the IoT and AI can greatly improve operational efficiency in all areas of application of ‘traditional’ information and communication technology. This may potentially result in the ability to project legacy systems into industry scale digital ecosystems catered on the creation of new data-driven services and digital experiences.

To best understand the applicability of the ‘grand vision’ expressed above, we now turn specifically to a review of recent scientific evidence on the use of AI in energy systems. A recent report from the International Renewable Energy Agency (IRENA 2019) on AI and big data states that for the power sector, digitalisation is essentially about converting data into value, which is also a consequence of advances in decentralisation and electrification. The report finds that most of the advances currently supported by AI have been in advanced weather and renewable power generation forecasting and in predictive maintenance. In the future, AI and big data will further enhance decision making and planning, condition monitoring, inspections, certifications and supply chain optimisation, and will generally increase the efficiency of energy systems.

However, studies in recent years have also focussed on the use of AI in battery development (Ziyi et al. 2020), to create mathematical models for the simulation of battery performance in electronic vehicles (Li et al. 2019) and an AI itself authored a book summarising ongoing research on lithium-ion batteries (Beta Writer 2019). There are also several potential applications of AI to energy systems optimisation. Since AI can be used to build prediction models, including changes in meteorological, social or economic contexts, more accurate short-term load energy forecasting may be achieved (Zor et al. 2017; Al Mamun et al. 2020; Solyali 2020). AI can also be used to identify both bottlenecks and investment opportunities in electricity grids based on load and temperature data (Park 1991), effectively providing simulation tests for different types and levels of investments using digital twins (Onile et al. 2021). AI could also be used to signal, in case of over- or under-supply within the grid, for relevant assets to charge or discharge supporting net balancing (Mbuwir et al. 2020). The latter may happen with or even without human involvement (Shen et al. 2018; Frendo et al. 2020).

Furthermore, AI can be applied to create a highly automated market for electricity. Electricity prices (and their fluctuations) can be estimated based on predicted supply and demand for electricity and near real time energy trading (Xu et al. 2019; Qiao – Yang 2020). Recent experiments with micro-grids also demonstrate that AI-based programs have the capacity to prevent oversupply on the electricity grid (Hou et al. 2019; Reijnders et al. 2020). The impacts of AI are also growing in sustainable energy systems. Applications include occupants' satisfaction and smart buildings (Ouahiba et al. 2018), IoT security and safety systems in smart cities (Zahmatkesh – Al-Turjman 2020), energy-saving with the use of the smart home concept (Pan et al. 2015), smart energy management (Zhou et al. 2020), improving wind turbine and unified power flow controller (UPFC) electrical stability (Dawn et al. 2019) and new solar technologies (Badiei et al., 2020).

3 AI and smart digital technologies beyond potential: exemplary case studies

Exemplary case studies are an essential tool for policymakers to understand the practical applications of AI and sustainable technologies in energy systems. To better appreciate the actual contributions of AI to energy systems, we combined the literature review presented above with a detailed analytical review of the Big Data and Artificial Intelligence-powered StartUs Insights Discovery Platform Report, covering 2.093.000+ start-ups and scale-ups globally (StartUs Insights 2022a). The latter produced a report analysing in detail 226 exemplary start-ups and scale-ups in 2022 (StartUs Insights 2022b). Based on data presented in the report as well as additional research, we discovered four Power-to-X, three sustainable technology start-ups and two concrete applications at the urban and municipal level from different geographies as exemplary case studies. Starting from our second proposition, the case studies have been selected based on their potential to be used as best practices to foster a common shared view among different policy makers on how to counteract high energy prices while at the same time curbing carbon emissions and addressing climate change. The exemplary case studies were selected from a variety of geographies iteratively as part of the data collection for the literature review, in order to represent the ‘very best’ of global initiatives within the key themes of the extant research in relation to the integration of artificial intelligence and sustainable technologies in the energy sector. The exemplary case studies selected consolidate the potential of AI and sustainable technologies in practical energy systems applications. Therefore, the case studies have the potential to provide policymakers with insights into the practical applications of AI and sustainable technologies in energy systems.

Table 1 displays the start-ups operating in renewable energy and Power-to-X. The four start-ups identified during our systematic review are Ineratec, SeeO2 Energy, Energeia and STOREH Energy Storage Technologies.

Table 1.

Notable Start-ups in renewable energy and Power-to-X

Ineratec – Power to LiquidSeeO2 Energy – Gas to FuelEnergeiaSTOREH Energy Storage Technologies
Swiss start-up Ineratec provides customizable applications of gas-to-liquid, power-to-gas, and power-to-liquid technologies. Their gas-to-liquid process converts fossil fuel emissions and renewable methane-containing gases into synthetic hydrocarbons and fuels.Canadian start-up SeeO2 Energy helps transform greenhouse gases (GHG) into assets by developing reversible fuel-cell technology. They convert synthetic gas, hydrogen, and carbon monoxide into alternative products like natural gas, methanol, ammonia, and synthetic liquid fuels.Indian start-up Energeia provides solutions for monitoring, identifying, financing, and implementing energy efficiency within a shared-savings business model.Italian start-up STOREH Energy Storage Technologies builds energy storage devices to solve the challenges arising from intermittent production and constant consumption of power. Their hydrogen on-demand (HOD) system produces hydrogen without requiring compressors and tanks.

Swiss start-up Ineratec provides customizable applications of gas-to-liquid, power-to-gas, and power-to-liquid technologies. Additionally, their power-to-liquid solution converts renewable electricity and carbon dioxide into liquid fuels and other chemicals (INERATEC 2022). Recently, INERATEC has been working on different projects on the upscaling and validation of reactors for producing carbon-neutral liquid fuels from enhanced biomass gasification (Biomass-to-Liquid) and from hydrogen from renewable power (Power-to-Liquid). The latter have concentrated on the economic viability of PtX processes while scaling-up the production capacity thanks to the application of gas analytics to their operations with minimal control efforts (Pfeifer et al. 2022).

Canadian start-up SeeO2 Energy helps transform greenhouse gases (GHG) into assets by developing reversible fuel-cell technology. Their technology also enables the conversion of CO2 into marketable and clean value-added fuels and chemicals. SeeO2 Energy is one of the top 5 carbon capture and storage start-ups identified in the StartUs report, selected specifically for their use of AI to convert synthetic gas, hydrogen, and carbon monoxide into alternative products like natural gas, methanol, ammonia, and synthetic liquid fuels (SeeO2 Energy 2022). SeeO2 Energy is notable also for their relatively early use of AI based tools such as Fullprof Software for material synthesis and characterisation of fuel cells applications (Sánchez et al. 2014).

Indian start-up Energeia provides solutions for monitoring, identifying, financing, and implementing energy efficiency within a shared-savings business model. Their analytical engine generates consumption trends and predicts potential energy management improvements by changes in the grid process (Energeia 2022a). Energeia uses AI and analytical models to digitise and optimise on-site energy production developing a model of the existing energy infrastructure, which can be modified to minimize the cost of energy (Energeia 2022b).

Italian start-up STOREH Energy Storage Technologies builds energy storage devices to solve the challenges arising from intermittent production and constant consumption of power. The company's storage solution also uses natural non-polluting materials such as zinc and water (StartUs Insights, 2022c). Storeh used AI and smart digital technologies to develop an energy storage and hydrogen on-demand (HOD) production system to solve the problem of the intermittency and non-programmability of renewable sources, making seasonal energy storage possible (STOREH 2022; Italgas 2022).

To further exemplify the potential of AI in energy systems, exemplary industry developments are summarised in Table 2, presenting potential benefits from integrating AI, data and analytics in the energy sector. For instance, the US-based start-up Iota provides EaaS that includes their BrightAI smart building software in combination with a myriad of energy conservation measures. The solution optimizes energy management that includes lighting, demand response, and renewable energy utilization (Iota Communications 2022).

Table 2.

Exemplary industry developments on the potential of AI

IotaJoS QuantumQC Ware
The US-based start-up Iota provides EaaS that includes their BrightAI smart building software in combination with a myriad of energy conservation measures. The solution optimizes energy management that includes lighting, HVAC, demand response, and renewable energy utilization.German startup JoS Quantum develops cloud-based software solutions for energy asset management. The quantum-enabled algorithms solve complex issues for the energy sector involving risk analysis, portfolio optimization, and machine learning (ML)-powered enhancements.The US-based startup QC Ware provides quantum computing solutions for optimizing energy utilization. Optimization and ML applications enable energy fault diagnosis, precise energy prediction, effective demand management, as well as asset risk analysis.

German start-up JoS Quantum develops cloud-based software solutions for energy asset management. The quantum-enabled algorithms solve complex issues for the energy sector involving risk analysis, portfolio optimization, and machine learning (ML)-powered enhancements (JoS Quantum 2022a). Together with Fraunhofer and the University of Heidelberg, JoS Qunatum is also developing new quantum algorithms that can solve the problem efficiently. Solving this problem efficiently can have a tremendous impact on modelling energy grids and pricing energy products (JoS Quantum 2022b; Fraunhofer Institute for Industrial Mathematics 2022).

Finally, the US-based start-up QC Ware provides quantum computing solutions for optimizing energy utilization. Optimization and ML applications enable energy fault diagnosis, precise energy prediction, effective demand management, as well as asset risk analysis (QC Ware 2022).

At the urban level scale two notable examples come from Sweden (Smart City Sweden 2022). Already in 2018, a Smart Grid Surveillance system developed by Exeri able to detect and accurately locate faults and standard deviations from the grid was installed in the electricity grid around Malå, Sweden. AI algorithms and advanced data analysis takes place thanks to smart sensors' surveillance and detection, resulting in reduced downtime and increases in operational efficiency. As illustrated in Fig. 1, smart sensors are mounted on grid poles and are constantly sending information to a central information collection and conclusion AI unit capable of real time insight into what is occurring in the grid (Exeri 2022).

Fig. 1.
Fig. 1.

Exeri's AI based smart grid surveillance system. Source: Exeri (2022).

Citation: Society and Economy 45, 4; 10.1556/204.2023.00012

At the Karlshamn municipality, AI has been applied to the district heating system. Thanks to the cooperation between the municipal energy company and NODA Systems, a leading private sector organization on AI-based innovation in district heating and cooling, the district heating system has become more efficient and sustainable. The project connected about 90 buildings used as virtual storage to Demand Side Management (DSM) through the NODA Heat Network in their district heating system. The flexibility provided from the connected buildings is used to reduce peak loads or during maintenance downtime. Self-learning AI based algorithms have also been used since 2019 to evenly match energy supply and demand based on actual heat demand in the district heating system (NODA 2022).

4 Risks, challenges & future prospects

AI-based data driven decision making and sustainable technologies are envisaged as possible solutions to afford public and private business organisations greater flexibility and greater market insight for the allocation of scarce resources in the energy industry. Yet, the aforementioned technologies have typically been underutilised due to dynamic complexity, multi-level, multi-actor, and multi-sectoral issues (Head – Alford 2013; Bianchi 2021) such as socio-economic challenges, the lack of education and awareness as well as the lack of governance, policy coordination and support from government institutions or the private sector. Furthermore, our review of the literature finds that only few areas of the potential developments identified in relation to the integration of artificial intelligence and sustainable technologies in energy systems are realised in practice, which implies that several new and existing risks and challenges remain to be fully delineated and investigated.

Ten interviews were scheduled with experts in the field to collect primary data to tackle specifically the third research question: what are the main risks and challenges associated with integrating artificial intelligence in energy systems and how can these risks and challenges be addressed? Experts were selected based on convenience sampling and included professionals from the energy industry as well as academics and consultants specialising in the use of AI in energy systems. Semi-structured interviews of about 30–40 min took place via Zoom to identify and explore the risks related to the use of artificial intelligence and to formulate proposals for risk management. In addition, our participation in the First Central European Power-to-Gas Conference (Regional perspectives of power-to-methane and carbon capture technologies), at the Hungarian Academy of Sciences between 12 and 13 December 2022, provided the possibility to collect additional primary data and to connect with professionals from both industry and academia, as well as to present and discuss a first draft of the paper. An initial draft of the compiled risks was shared with all interviewees to gather feedback which was then included in the final version of the risk impact matrix of AI integration in energy systems presented in Table 4.

What emerges from the data is that the contribution of AI and sustainable technologies in the energy sector has a high potential, but it is yet to fully realise it according to the scientific evidence and the industry practices reviewed above, especially in PtX projects where virtually no known actual AI applications have been yet identified. However, even in the exemplary case studies on the use of AI, a key contribution to AI models are existing energy models. The latter have been developed in the industry prior to the contemporary interest in AI in relation to energy, sustainability and climate issues. Since their inception, energy economy models still in use today have been designed and developed to operate under conditions of data scarcity (or containing critical gaps), uncertainty on future conditions (Lempert et al. 2002), and insufficient information to reliably apply formal statistical methods (Li et al. 2019).

Crucially, we would add, such models are also based on a radically different implicit operating model of the energy system, one based on centralisation and a small number of energy producers rather than decentralisation of energy production and distribution across several producers-consumers as it is the case in renewable energy generation. The latter can result in several challenges as well as risks. For instance, while a balancing of the electricity grid according to fluctuations in market price is already in effect, it does not operate perfectly since an oversupply of (renewably generated) electricity may be curtailed instead of saved (Niet et al. 2021). Likewise, while AI has the potential to facilitate grid management, increase flexibility in asset management and electricity market activities, there are also associated risks to be considered including the lack of transparency, a decline of human autonomy, cybersecurity, market dominance, and price manipulation on the electricity market (Burke – O'Malley, 2011; Bird et al. 2016; Interviews#2, 5, 6, 7).

The potential and best practices discussed in this paper rely on the possibility to directly collect, observe and measure the real-world intersection of energy supply and demand and the economy, to create valid energy economy models containing hundreds of different parameters. According to Li et al. (2019): ‘as with most applied models in science, this means that energy economy models typically represent a hybrid between observed data and theories about the functioning of the world’. Table 3 gives an overview of some of the blind spots in existing energy economy models that are caused by data scarcity, together with examples of contemporary practice and the challenges that this raises for policy research.

Table 3.

Blind spots and challenges in existing energy economy models that are caused by data scarcity

Data typesStatus quoChallenges
SpatialInsights typically focused at the national or global scale, with highly aggregated geographical coverage. Some models are multi-region but only representing large regions e.g. USA, China, India, Europe in global models.Spatial aspects of energy transitions (infrastructure, location of resources, local decision making, socio-cultural differences) are difficult to represent without detailed spatial disaggregation.
TechnologicalTypical practice is to aggregate the engineering detail of different types of physical stocks (buildings, power stations, vehicles, industrial plants etc.) into broad categories of representative technologies.Technology aggregation can lead to simplification of the existing energy asset base, and not adequately differentiate on cost/performance grounds between new technology investments.
Socio-economic and demographicMany models employ an abstract representation of an “average” person per country, which can hide significant differences in energy use across socio-economic groups e.g. income, age, household size, behavioural characteristics etc.Different social characteristics are all linked to varying patterns of energy use, and it is challenging to capture this in models without the data to study or characterise these relationships. It is also challenging to explore the distributional impacts of different policies.
Type and condition of existing stock (physical assets)In many countries, there is a fundamental lack of data to characterise the energy system in many sectors. This is typically the case in countries with limited resources to develop and maintain statistical databases.A lack of detailed data can make it challenging to target policies and resources at specific stocks, e.g. improving conditions for vulnerable energy users in poorly insulated homes, retrofitting old and polluting industries, or targeting older and less efficient vehicles.

Source: author, adapted from Li et al. (2019).

In principle, big data may provide new opportunities for diminishing the blind spots and increasing the coverage of information to feed AI based energy models. Some of the potential sources of data that may become available thanks to sustainable technologies include real-time data and feedback from electric power grids (Schuelke et al. 2015), residential smart utility meters data (Shahrokni 2014), advanced databases on buildings (Mathew et al. 2015) satellite imaging (Wang et al. 2015) and social data from the world wide web (Pearce et al. 2014). In addition, spatial and temporal resolution data could potentially give decision makers a clearer idea of how energy is being used and produced at a very granular level with very fine time slices (Li et al. 2019). The latter could unlock the door to multiple possibilities for policymaking, possibly even in real time. Such increase in data availability could further also increase the accuracy of data model structuring and parameterization, whose potential to improve energy economy modelling has been vastly overlooked in the existing literature (Li et al. 2019).

The increasing availability of the sources of data above, when combined with advances in machine learning, artificial intelligence and cluster analysis (Blanford et al. 2018), potentially opens up new insights and horizons in the energy modelling landscape. For instance, detailed real time energy consumption models may be built based on smart meter data from a heterogeneous population rather than based on ‘average’ usage patterns (Albert – Rajagopal 2013). The latter may then support policy interventions aimed at demand side response measures: optimizing electric vehicle (EV) charging (Arias – Bae 2016) or electricity exchange based on solar energy produced via solar panels on residential roofs. Policy may be further enabled once spatial and temporal data beyond the household level may become available to distill the consumption profiles of individual appliances (Weiss et al. 2012), further supporting policies at the appliance level.

Nevertheless, the increase in complexity and sophistication of energy systems resulting from the integration of AI and sustainable technologies are redefining how energy systems are designed, built and operated which may raise also new security concerns both according to Nielsen et al. (2017) as well as experts interviewed. To be sure, the deployment of AI, renewables and PtX are primary enablers of a large scale shift in energy production, retiring conventional generation based on inverted-based resources in favor of energy storage, which was not part of the history of the electric grid (US Department of Energy 2022). Significant change is also happening at the ‘edge’ of the grid with home and business owners. For instance, via a mobile device, it is possible to manage a number of applications to monitor home conditions (regulate the temperature, switch on/off devices, etc.), The IoT and sensors would be always on, sending dense data over the Internet. However, if on the one hand the latter allows for the introduction of pricing and incentives based on demand response (Haider et al. 2016), on the other hand, the extent of data integration required may create new areas of vulnerability (Interviews#1 to 10).

For example, according to the majority of the experts interviewed, it may become easier for hackers to penetrate the cyber system of energy grids remotely and to perform actions that may not be normally authorised. Such actions may include data flow manipulations or blocks and obscuring the operation of the grid, thus compromising the safety, stability and efficiency of power supplies. Manipulation of real-time measurements may lead the control center to make decisions that would not be appropriate or even force field devices to trigger autonomously unnecessary control actions.

According to the experts interviewed, there may be several ‘unintended consequences’ (Interview#3), including denial of service attacks that could nullify the effect of control signals to take remedial action in the physical processes involved in power generation. Weak points should be identified as well as who could tamper with the data (Interview#4). For instance, hackers could even modify the configuration of the grid (e.g., line ratings, characteristics, protective settings) and inflict serious physical consequences on grid operations that could include degradation and even interruption of power services. A cyber incident may also cause a cascading effect of malfunctions in the cyber system because of the lack of power needed for the operation of networking services and IoT devices, short-circuiting both the virtual and the physical operations of the electricity grid, especially critical in load forecasting systems (Interview#2). Therefore, it is important to focus on fail-safety and to retain the ability for a manual override when necessary (Interview#6).

Additional risks may also result from data transfer time latency, which could compromise the energy system protection function and challenge the mitigation of localised faults in the network, potentially spreading a power outage further. Extreme weather events may also compromise the operations of key sensors placed on power components to monitor any failure or malfunction, reducing the effectiveness of the control centre to predict outages and minimise further damages to the power system.

Risks unveiled via the collection of primary and secondary data are summarised in the risk impact matrix presented in Table 4, outlining each risk's likelihood as well as the impact. For each risk/risk type, a risk impact and mitigation table has also been compiled to formulate proposals for risk management, including controls on algorithms; independent reviews of AI model results; conceptual controls; transparency requirements; enterprise-wide controls for data model biases as well as robust risk governance processes; real time monitoring systems for prompt response on identified risks and vulnerabilities; ongoing training and awareness for staff, contractors and stakeholders (Table 5).

Table 4.

Risks impact matrix of AI integration in energy systems

Source: author.

Table 5.

Risks, impact, likelihood and risk management response for AI integration in energy systems for high and medium risks

RiskRisk impactRisk likelihoodRisk management response
Natural/man-made disasters, market dominanceHighLowIntroduce disaster recovery planning protocols; support promotion of fair market outcomes via independent assessments
Biases & fairness, governance issues, robustness, complexity, price manipulationMediumMediumEnforce controls on algorithms; independent reviews of model results; conceptual controls; transparency requirements; enterprise-wide controls for model biases
Practical case experience availabilityLowHighCreate enterprise-wide approved use-cases based on recognised principles
Unintended systemic consequences, precision quality, decline of human autonomyHighMediumAssess impartially the aim of the model, suggested analytical techniques, expected factors, and intended applications; user training on when and how to override AI outputs
Privacy, lack of transparencyMediumHighAvoid overly complex/opaque models; frequent model performance assessments
Cyber attacks, data quality, scarcity of skilled staffHighHighIntroduce robust risk and IT governance processes; real time monitoring systems for prompt response on identified risks and vulnerabilities; ongoing training and awareness for staff, contractors and stakeholders

Source: author.

5 Summary of goals, research questions and results for each proposition

To sum up, the digitisation of the electricity grid via the introduction of AI and sustainable digital technologies would transform it into a typical cyber-digital system, with THE increasing reliance of physical processes on advanced technology. While sustainable technologies can certainly promote and introduce a number of beneficial transformations towards increasing energy efficiency and actions to mitigate climate change, they also introduce a number of new threats and high impact/high likelihood risks to operations such as unintended systemic consequences, precision quality, decline of human autonomy, privacy, lack of transparency, cyber attacks, data quality and scarcity of skilled staff that need to be assessed prior to the full scale realisation of their vast potential identified in this paper. Table 6 presents a summary table of goals, research questions, propositions, case study/sources, key points and results, in order to answer research question.

Table 6.

Summary table of goals, research questions, propositions, case study/sources, key points to answer research question

GoalsResearch questionPropositionCase study/sourceKey points to address goals/answer research questionResults for each proposition
1–4What role AI and sustainable technologies play in strategic renewable energy and Power-to-X Projects?The integration of AI and sustainable technologies in strategic renewable energy and Power-to-X projects can support energy efficiency and systemic improvements in the energy sector in the EU region and globallyIneratec, SeeO2 Energy, Energeia, STOREH Energy Storage Technologies, high level interviewsCustomizable applications, gas analytics, AI used for conversion of CO2 into marketable and clean value-added fuels and chemicals;

AI and analytical models used to digitise and optimise on-site energy production developing a model of the existing energy infrastructure;

AI and smart digital technologies to develop an energy storage and hydrogen on-demand ‘HOD’ production system
The case studies demonstrate the potential of AI to support energy efficiency and systemic improvements in the energy sector, therefore the proposition is supported.
1–6In what way can best practices and exemplary case studies be used to foster a common shared view among different policy makers?Best practices and exemplary case studies can be used to foster a common shared view among different policy makers on how to counteract high energy prices while at the same time curbing carbon emissions and addressing climate change.Iota, JoS QUANTUM, QC Ware, smart grid surveillance system in Malå Sweden, district heating system in Karlshamn Sweden, high level interviewsBrightAI smart building software optimizes energy management;

Quantum-enabled algorithms can solve complex issues for the energy sector involving risk analysis, portfolio optimization, and machine learning (ML)-powered enhancements; Optimization and ML applications enable energy fault diagnosis, precise energy prediction, effective demand management, as well as asset risk analysis;

AI algorithms and advanced data analysis result in reduced downtime and increases in operational efficiency;

Self-learning AI based algorithms have been used since 2019 to evenly match energy supply and demand based on actual heat demand in district heating
The case studies demonstrate how best practices and exemplary case studies can be used to foster a common shared view among different policy makers, therefore the proposition is supported.
5–6What are the main risks and challenges associated with integrating AI in energy systems and how can these risks and challenges be addressed?The main risks and challenges associated with integrating AI in energy systems include energy transitions, assumptions about human behaviour, lack of detailed data and cybersecurity.Literature review, case studies, high level interviews with AI experts globallyThe main risks and challenges identified are unintended systemic consequences, precision quality, decline of human autonomy, privacy, lack of transparency, cyber attacks,data quality, scarcity of skilled staff.

Risk management response proposals have been outlined for each main type of risk.
The proposition is only partially supported since primary data collection has unveiled several high and medium likelihood and impact risks that were not initially identified via secondary data sources.

Source: author.

All of the initial goals of the paper have been addressed. The three initial propositions were the following:

  1. The integration of AI and sustainable technologies in strategic renewable energy and Power-to-X projects can support energy efficiency and systemic improvements in the energy sector in the EU and globally.

  2. Best practices and exemplary case studies can be used to foster a common shared view among different policy makers on how to counteract high energy prices while at the same time curbing carbon emissions and addressing climate change.

  3. The main risks and challenges associated with integrating artificial intelligence in energy systems include energy transitions, assumptions about human behaviour, lack of detailed data and cybersecurity

Of these, only the first two are fully supported according to the results of the study. The third proposition is only partially supported, since primary data collection from interviews has unveiled several high and medium likelihood and impact risks that were not initially identified via secondary data sources.

6 Conclusion, recommendations and future research

This paper presents a systematic review focussed on the scientific evidence available in relation to the integration of AI and sustainable technologies in strategic renewable energy and Power-to-X projects. We find that the potential of projects in the aforementioned areas are seldom realised in practice, whereas research has focussed mostly on the technological aspects rather than the business, policy or risk considerations stemming from the integration of AI and sustainable technology in the energy sector.

Several concrete applications of the integration of AI and sustainable technologies were discovered and discussed as possible mechanisms to enhance policies on energy systems' operations in support of systemic sustainable improvements in the EU and globally. The main risks unveiled include unintended systemic consequences, precision quality, decline of human autonomy, privacy, lack of transparency, cyber attacks, data quality and scarcity of skilled staff. the latter, combined with a move towards distributed energy production and distribution, can result in critical risks stemming both from blind spots in existing energy economy models as well as the increase in complexity and sophistication of energy systems in the move towards decentralized energy systems.

Therefore, a focus on security, affordability and innovation in systemic modelling approaches based on multiple areas of impact and results should be part of the strategic vision to be pursued by policy makers beyond the stated potential of the latest trending technologies or narrow views on ‘secure data transfer’ practices. Based on the literature reviewed, the primary evidence gathered, and the risk management and mitigation proposals outlined, several areas of future development for academic research, as well as opportunities for professional practice, remain to be further investigated. Particularly, considering that future policies and practices should:

  • support resilience by design in energy systems and grid operation promoting cyber security;

  • introduce mechanisms to facilitate seamless data interoperability and integration while guaranteeing data consistency and fail-safe validation;

  • design sustainable technologies implementing corresponding processes to provide reliable energy supply while addressing climate change;

  • develop AI applications in consideration of new energy models and dynamics.

Finally, developing effective governance arrangements between the various stakeholders involved from the public and private sectors is paramount and requires a dynamic understanding of the several organisations, components, factors, structures and infrastructures and their interactions in cyberspace as well as their physical repercussions. Among promising future research directions, system dynamics theory and modelling can offer the opportunity to further explore both risks and challenges, as well as exciting new opportunities in view of building effective interactions between industry, academia, and government. System dynamics can also foster a timely understanding of the non-linear behaviour emerging from the integration of artificial intelligence and sustainable technologies in strategic renewable energy and Power-to-X projects in the EU region and globally to best address multi-level, multi-actor, and multi-sectoral challenges (Head – Alford 2013; Bianchi 2021). The latter can provide a framework to broaden the future impact of policies and initiatives not only towards a deeper appreciation of potential benefits arising from operational efficiency, but also stemming from the ideation of new creative data-driven approaches and solutions that can help reinvent legacy energy systems for the achievement of the EU action plan for digitizing the energy system as well as the Sustainable Development Goals championed by the United Nations.

Acknowledgement1

The constructive comments and suggestions of the two anonymous reviewers and the journal editor are gratefully thanked and acknowledged. The author would also like to thank EU Business School for granting the EU Research Scholarship in June 2023.

References

  • Albert, A.Rajagopal, R. (2013): Smart Meter Driven Segmentation: what Your Consumption Says about You. IEEE Transactions on Power Systems 28(4): 40194030.

    • Search Google Scholar
    • Export Citation
  • Arias, M. B.Bae, S. (2016): Electric Vehicle Charging Demand Forecasting Model Based on Big Data Technologies. Applied Energy 183: 327339.

    • Search Google Scholar
    • Export Citation
  • Badiei, A.Golizadeh Akhlaghi, Y.Zhao, X.Shittu, S.Xiao, X.Li, J. (2020): A Chronological Review of Advances in Solar Assisted Heat Pump Technology in 21st Century. Renewable and Sustainable Energy Review 132: 110132.

    • Search Google Scholar
    • Export Citation
  • Barbaresi, A.Morini, M.Gambarotta, A. (2022): Review on the Status of the Research on Power-To-Gas Experimental Activities. Energies 15(16): 5942.

    • Search Google Scholar
    • Export Citation
  • Beta Writer (2019): Lithium-Ion Batteries: A Machine-Generated Summary of Current Research. Cham: Springer.

  • Bianchi, C. (2021): Fostering Sustainable Community Outcomes through Policy Networks: A Dynamic Performance Governance Approach. In: Weine, J. (ed.): Handbook of Collaborative Public Management. Edward Elgar Publishing.

    • Search Google Scholar
    • Export Citation
  • Bird, L.Lew, D.Milligan, M.Carlini, E. M.Estanqueiro, A.Flynn, D. (2016): Wind and Solar Energy Curtailment: A Review of International Experience. Renewable and Sustainable Energy Review 65: 577586.

    • Search Google Scholar
    • Export Citation
  • Bofinger, B. (2022): What Are Power-To-X Solutions? https://as-schneider.blog/2022/03/02/what-are-power-to-x-solutions/, accessed 21/10/2022.

    • Search Google Scholar
    • Export Citation
  • Blanford, G. J.Merrick, J. H.Bistline, J. E. T.Young, D. T. (2018): Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection. Energy Journal 39(3): 189212.

    • Search Google Scholar
    • Export Citation
  • Burke, D. J.O'Malley, M. J. (2011): Factors Influencing Wind Energy Curtailment. IEEE Transactions on Sustainable Energy 2(2): 185193.

    • Search Google Scholar
    • Export Citation
  • Columbia University (2022): Europe’s Winter Outlook. 2022. SIPA Center on Global Energy Policy. https://www.energypolicy.columbia.edu/europe-s-winter-outlook, accessed 07/11/2022.

    • Search Google Scholar
    • Export Citation
  • Dawn, S.Tiwari, P.K.Goswami, A.K. (2019): An Approach for Long Term Economic Operations of Competitive Power Market by Optimal Combined Scheduling of Wind Turbines and FACTS Controllers Energy 181(C): 709723.

    • Search Google Scholar
    • Export Citation
  • Energeia (2022a): Energy Monitoring. https://www.energeia.in/ems, accessed 08/11/2022.

  • Energeia (2022b): Vision. https://www.energeia.in/vision, accessed 08/11/2022.

  • EUR-Lex (2022): Digitalising the Energy System - EU Action Plan. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52022DC0552&qid=1666369684560. 30/10/2022.

    • Search Google Scholar
    • Export Citation
  • Exeri (2022): Smart Grid Surveillance TM. https://www.exeri.se/smart-grid-surveillance#sgs/the-technology, accessed 08/11/2022.

  • Fraunhofer Institute for Industrial Mathematics (2022): EnerQuant: Quantum Computing for the Power Industry. Fraunhofer ITWM. https://www.itwm.fraunhofer.de/en/departments/fm/power-industry/enerquant-quantencomputing-power-industry.html, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • Frendo, O.Graf, J.Gaertner, N.Stuckenschmidt, H. (2020): Data-driven Smart Charging for Heterogeneous Electric Vehicle Fleets. Energy and AI 1: 100007.

    • Search Google Scholar
    • Export Citation
  • Haider, H. T.See, O. HElmenreich, W. (2016): A Review of Residential Demand Response of Smart Grid. Renewable Sustainable Energy Review 59: 166178.

    • Search Google Scholar
    • Export Citation
  • Head, B.Alford, J. (2013): Wicked Problems: Implications for Public Policy and Management. Administration & Society 47(6): 711739.

    • Search Google Scholar
    • Export Citation
  • Hou, W.Guo, L.Ning, Z. (2019): Local Electricity Storage for Blockchain-Based Energy Trading in Industrial Internet of Things. IEEE Transactions on Industrial Informatics 15: 36103619.

    • Search Google Scholar
    • Export Citation
  • INERATEC (2022): Processes. https://ineratec.de, accessed 08/11/2022.

  • Iota Communications (2022): Provider of Internet of Things Connectivity and Energy Solutions. https://www.iotacommunications.com, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • IRENA (2019): Artificial Intelligence and Big Data: Innovation Landscape Brief. International Renewable Energy Agency. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Sep/IRENA_AI_Big_Data_2019.pdf?, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • Italgas (2022): Italgas Innovation: Digital Transformation. https://www.italgas.it/en/group/innovation-digital-transformation/, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • JoS QUANTUM (2022a): Quantum Computing for Capital and Energy Markets. https://jos-quantum.de/, accessed 08/11/2022.

  • JoS QUANTUM (2022b): Data Science with Advanced Technologies. https://jos-quantum.de/, accessed 08/11/2022.

  • Lempert, R.Popper, S.Bankes, S. (2002): Confronting Surprise. Social Science Computer Review (20): 420440.

  • Levi-Jakšić, M.Rakićević, J.Jovanović, M. (2018): Sustainable Technology and Business Innovation Framework: A Comprehensive Approach. Amfiteatru Economic Journal 20(48): 418436.

    • Search Google Scholar
    • Export Citation
  • Li, F. G.Bataille, C.Pye, S.O'Sullivan, A. (2019): Prospects for Energy Economy Modelling with Big Data: Hype, Eliminating Blind Spots, or Revolutionising the State of the Art? Applied Energy 239: 9911002.

    • Search Google Scholar
    • Export Citation
  • Mamun, A. A.Sohel, M.Mohammad, N.Haque Sunny, M. S.Dipta, D. R.Hossain, E. (2020): A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models. IEEE Access 8: 134911134939.

    • Search Google Scholar
    • Export Citation
  • Mathew, P. A.Dunn, L. N.Sohn, M. D.Mercado, A.Custudio, C.Walter, T. (2015): Big-data for Building Energy Performance: Lessons from Assembling a Very Large National Database of Building Energy Use. Applied Energy 140(C): 8593.

    • Search Google Scholar
    • Export Citation
  • Mbuwir, B. V.Geysen, D.Spiessens, F.Deconinck, G. (2020): Reinforcement Learning for Control of Flexibility Providers in a Residential Microgrid. IET Smart Grid 3(1): 98107.

    • Search Google Scholar
    • Export Citation
  • Navarra, D. (2022a): Strategic Renewable Energy and Power-To-X Projects: A Review of Best Practices, Risks and Challenges for Integrating Artificial Intelligence and Smart Sustainable Technologies. First Central European Power-to-Gas Conference: Regional Perspectives of Power-to-Methane and Carbon Capture Technologies. Hungarian Academy of Sciences, Budapest, Hungary, 12–13 December.

    • Search Google Scholar
    • Export Citation
  • Navarra, D. (2022b): Sustainable Technology, AI and the Localisation of SGDs: A Research Perspective on Smart Agriculture and the Insurance Industry in Africa. Panel on Improving Policy Analysis and Performance Governance through Outcome Oriented Approaches to “localize” the SDG Agenda, Asian Association for Public Administration (AAPA) Conference, Shanghai, China, December 3–4.

    • Search Google Scholar
    • Export Citation
  • Nielsen, J. J.Ganem, H.Jorguseski, L.Alic, K.Smolnikar, M.Zhu, Z.Pratas, N. K.Golinski, M.Zhang, H.Kuhar, U. (2017): Secure Real-Time Monitoring and Management of Smart Distribution Grid Using Shared Cellular Networks. IEEE Wireless Communications 24: 1017.

    • Search Google Scholar
    • Export Citation
  • Niet, I.van Est. R.Veraart, F. (2021): Governing AI in Electricity Systems: Reflections on the EU Artificial Intelligence Bill. Frontiers in Artificial Intelligence 4(July): 17.

    • Search Google Scholar
    • Export Citation
  • NODA (2022): Case Studies. https://noda.se/casestudies/, accessed 19/11/2022.

  • Onile, A. E.Machlev, R.Petlenkov, E.Levron, Y.Belikov, J. (2021): Uses of the Digital Twins Concept for Energy Services Intelligent Recommendation Systems, and Demand Side Management: A Review. Energy Reports 7: 9971015.

    • Search Google Scholar
    • Export Citation
  • Ouahiba, T.Fatima, B.Thafath, H. (2018): Smart Buildings and Occupants Satisfaction: The Case of Cyber Park of Sidi Abdallâh and Some Residential Buildings in Algeria. In: Hatti, M. (ed.) Artificial Intelligence in Renewable Energetic Systems. Lecture Notes in Networks and Systems. Cham: Springer.

    • Search Google Scholar
    • Export Citation
  • Pan, J.Jain R.Paul S.Vu T.Saifullah A.Sha M. (2015): An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments. IEEE Internet of Things Journal 2(6): 527537.

    • Search Google Scholar
    • Export Citation
  • Park, D. C.El-Sharkawi, M. A.Marks, R. J.Atlas, L. E.Damborg, M. J. (1991): Electric Load Forecasting Using an Artificial Neural Network. IEEE Transactions on Power Systems 6(2): 442449.

    • Search Google Scholar
    • Export Citation
  • Pearce, W.Holmberg, K.Hellsten, I.Nerlich, B. (2014): Climate Change on Twitter: Topics, Communities and Conversations about the 2013 IPCC Working Group 1 Report. Plos One 9(4): 111.

    • Search Google Scholar
    • Export Citation
  • Pfeifer, P.Schmidt, S.Betzner, F.Kollmann, M.Loewert, M.Böltken, T.Piermartini, P. (2022): Scale-up of Microstructured Fischer–Tropsch Reactors – Status and Perspectives. Current Opinion in Chemical Engineering 36: 100776.

    • Search Google Scholar
    • Export Citation
  • QC Ware (2022): QC Ware Solutions. https://www.qcware.com/solutions, accessed 19/11/2022.

  • Qiao, W.Yang, Z. (2020): Forecast the Electricity Price of U.S. Using a Wavelet Transform-Based Hybrid Model. Energy 193: 116704.

  • Rani, P.Mishra, A. R.Saha, A.Pamucar, D. (2021): Pythagorean Fuzzy Weighted Discrimination-Based Approximation Approach to the Assessment of Sustainable Bioenergy Technologies for Agricultural Residues. International Journal of Intelligent Systems 36(6): 29642990.

    • Search Google Scholar
    • Export Citation
  • Reijnders, V. M. J. J.van der Laan, M. D.Dijkstra, R.Sioshansi, F. P. (2020): Energy Communities: a Dutch Case Study. In: Sioshansi, F. P. (ed.): Behind and beyond the Meter. Digitalization, Aggregation, Optimization .San Francisco: Academic Press.

    • Search Google Scholar
    • Export Citation
  • Sánchez, M.Prado-Gonjal, B.Ávila-Brande, J.Chen, D.Morán, M.Viola, E. B. (2014): High Performance La0.3Ca0.7Cr0.3Fe0.7O3-D Air Electrode for Reversible Solid Oxide Fuel Cell Applications. International Journal of Hydrogen Energy 40(4): 19021910.

    • Search Google Scholar
    • Export Citation
  • Schuelke-Leech, B. A.Barry, B.Muratori, M.Yurkovich, B. J. (2015): Big Data Issues and Opportunities for Electric Utilities. Renewable Sustainable Energy Review 52(C): 937947.

    • Search Google Scholar
    • Export Citation
  • SeeO2 Energy (2022): What We Do. https://www.seeo2energy.com/what-we-do/, accessed 19/11/2022.

  • Shafiei, M. W.Abadi, H. (2017): The Importance of Green Technologies and Energy Efficiency for Environmental Protection. International Journal of Applied Environmental Sciences 12(5): 937951.

    • Search Google Scholar
    • Export Citation
  • Shahrokni, H.Levihn, F.Brandt, N. (2014): Big Meter Data Analysis of the Energy Efficiency Potential in Stockholm’s Building Stock. Energy Build 78: 153164.

    • Search Google Scholar
    • Export Citation
  • Shen, J.Zhou, T.Wei, F.Sun, X.Xiang, Y. (2018): Privacy-preserving and Lightweight Key Agreement Protocol for V2G in the Social Internet of Things. IEEE Internet Things Journal 5(4): 25262536.

    • Search Google Scholar
    • Export Citation
  • Smart City Sweden (2022): The Intelligent District Heating System – Makes Use of AI. Best practice - Smart City Sweden. https://smartcitysweden.com/best-practice/430/the-intelligent-district-heating-system-makes-use-of-ai/, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • Solyali, D. (2020): A Comparative Analysis of Machine Learning Approaches for Short-/long-Term Electricity Load Forecasting in Cyprus. Sustainability 12(9): 3612.

    • Search Google Scholar
    • Export Citation
  • StartUs Insights (2022a): Explore 2+ Mio. Startups & Scaleups: StartUs Insights Discovery Platform. https://www.startus-insights.com/startus-insights-platform/, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • StartUs Insights (2022b): Heat Map: 5 Top Carbon Capture & Storage Startups. https://www.startus-insights.com/wp-content/uploads/2020/06/Carbon-Capture-_-Storage-Startups-Energy-Heat-Map-StartUs-Insights-noresize.png, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • StartUs Insights (2022c): Top 10 Energy Industry Trends & Innovations. https://www.startus-insights.com/innovators-guide/top-10-energy-industry-trends-innovations-in-2021/, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • Stirling, A. (2007): Deliberate Futures: Precaution and Progress in Social Choice of Sustainable Technology. Sustainable Development 15(5): 286295.

    • Search Google Scholar
    • Export Citation
  • STOREH (2022): Hydrogen on Demand, Energy Storage Device. https://store-h.com/, accessed 19/11/2022.

  • Stritch, J. M.Darnall, N.Hsueh, L.Bretschneider, S. (2018): Green Technology Firms and Sustainable Public Purchasing. IEEE Engineering Management Review 46(1): 128131.

    • Search Google Scholar
    • Export Citation
  • Subramaniam, M. (2022): The Future of Competitive Strategy: Unleashing the Power of Data and Digital Ecosystems. The MIT Press.

  • US Department of Energy (2022): Cybersecurity Considerations for Distributed Energy Resources on the U.S. Electric Grid .https://www.energy.gov/sites/default/files/2022-10/Cybersecurity%20Considerations%20for%20Distributed%20Energy%20Resources%20on%20the%20U.S.%20Electric%20Grid.pdf, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • Wang, S.Zhang, Q.Martin, R. V.Philip, S.Liu, F.M. Li (2015): Satellite Measurements Oversee China’s Sulfur Dioxide Emission Reductions from Coal-Fired Power Plants. Environmental Research Letters 10(11): 114015.

    • Search Google Scholar
    • Export Citation
  • Weiss, M.Helfenstein, A.Mattern, F.Staake, T. (2012): Leveraging Smart Meter Data to Re- Cognize Home Appliances. 2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, Switzerland.

    • Search Google Scholar
    • Export Citation
  • Wulf, C.Zapp, P.Schreiber, A. (2020): Review of Power-To-X Demonstration Projects in Europe. Frontiers in Energy Research 8: 112.

    • Search Google Scholar
    • Export Citation
  • Xu, Y.Ahokangas, P.Louis, J. N.Pongrácz, E. (2019): Electricity Market Empowered by Artificial Intelligence: A Platform Approach. Energies 12(21): 4128.

    • Search Google Scholar
    • Export Citation
  • Zahmatkesh H.Al-Turjman F. (2020): Fog Computing for Sustainable Smart Cities in the IoT Era: Caching Techniques and Enabling Technologies - an Overview. Sustainable Cities and Society 59: 102139.

    • Search Google Scholar
    • Export Citation
  • Zhou, Y.Zhai, Q.Zhou, M.Li, X. (2020): Generation Scheduling of Self-Generation Power Plant in Enterprise Microgrid with Wind Power and Gateway Power Bound Limits, IEEE Transactions on Sustainable Energy 11(2020): 758770.

    • Search Google Scholar
    • Export Citation
  • Ziyi, L.Xinyi, Y.Yingxue, W.Weidi, L.Siliang, L.Yuankun, Z.Zihan, H.Hong, Z.Shuming, D.Jie, X.Jiachen, J.Kui X.Xiaowang, Z.Wenbin, H.Yida, D. (2020): A Survey of Artificial Intelligence Techniques Applied in Energy Storage Materials R&D. Frontiers in Energy Research 8: 116.

    • Search Google Scholar
    • Export Citation
  • Zor, K.Timur, O.Teke, A. (2017): A State-Of-The-Art Review of Artificial Intelligence Techniques for Short-Term Electric Load Forecasting. 6th International Youth Conference on Energy. Budapest, Hungary.

    • Search Google Scholar
    • Export Citation

List of Interviews

  • Interview#1: Amos Agbetile, Electrical Engineer, 21 April 2023, online.

  • Interview#2: Professor Pierluigi Siano, Scientific Director of the Smart Grids and Smart Cities Laboratory, Department of Management & Innovation Systems, University of Salerno, 27 April 2023, online.

    • Search Google Scholar
    • Export Citation
  • Interview#3: Richard Gardner, CEO, Modulus, 27 April 2023, online.

  • Interview#4: Dr Eva-Marie Muller-Stuler, Partner EY Data & Analytics, 28 April 2023, online.

  • Interview#5: Dr Roger Miles, Oxford Scholar, 4 May 2023, online.

  • Interview#6: Dr Ansgar Koene, Global AI Ethics and Regulatory Leader at EY, 5 May 2023, online.

  • Interview#7: Vice President, Data Science and Artificial Intelligence, anonymous company, 8 May 2023, online.

  • Interview#8: Head of Artificial Intelligence, Anonymous Company, 10 May 2023, online.

  • Interview#9: Dr Bruno Zarpelao, Associate Professor at State University of Londrina (Brazil), 11 May 2023, online.

  • Interview#10: Maria Axente, Responsible AI Executive, PwC, 1 June 2023, online.

1

The acknowledgement was amended upon request of the author on 11 September 2023.

  • Albert, A.Rajagopal, R. (2013): Smart Meter Driven Segmentation: what Your Consumption Says about You. IEEE Transactions on Power Systems 28(4): 40194030.

    • Search Google Scholar
    • Export Citation
  • Arias, M. B.Bae, S. (2016): Electric Vehicle Charging Demand Forecasting Model Based on Big Data Technologies. Applied Energy 183: 327339.

    • Search Google Scholar
    • Export Citation
  • Badiei, A.Golizadeh Akhlaghi, Y.Zhao, X.Shittu, S.Xiao, X.Li, J. (2020): A Chronological Review of Advances in Solar Assisted Heat Pump Technology in 21st Century. Renewable and Sustainable Energy Review 132: 110132.

    • Search Google Scholar
    • Export Citation
  • Barbaresi, A.Morini, M.Gambarotta, A. (2022): Review on the Status of the Research on Power-To-Gas Experimental Activities. Energies 15(16): 5942.

    • Search Google Scholar
    • Export Citation
  • Beta Writer (2019): Lithium-Ion Batteries: A Machine-Generated Summary of Current Research. Cham: Springer.

  • Bianchi, C. (2021): Fostering Sustainable Community Outcomes through Policy Networks: A Dynamic Performance Governance Approach. In: Weine, J. (ed.): Handbook of Collaborative Public Management. Edward Elgar Publishing.

    • Search Google Scholar
    • Export Citation
  • Bird, L.Lew, D.Milligan, M.Carlini, E. M.Estanqueiro, A.Flynn, D. (2016): Wind and Solar Energy Curtailment: A Review of International Experience. Renewable and Sustainable Energy Review 65: 577586.

    • Search Google Scholar
    • Export Citation
  • Bofinger, B. (2022): What Are Power-To-X Solutions? https://as-schneider.blog/2022/03/02/what-are-power-to-x-solutions/, accessed 21/10/2022.

    • Search Google Scholar
    • Export Citation
  • Blanford, G. J.Merrick, J. H.Bistline, J. E. T.Young, D. T. (2018): Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection. Energy Journal 39(3): 189212.

    • Search Google Scholar
    • Export Citation
  • Burke, D. J.O'Malley, M. J. (2011): Factors Influencing Wind Energy Curtailment. IEEE Transactions on Sustainable Energy 2(2): 185193.

    • Search Google Scholar
    • Export Citation
  • Columbia University (2022): Europe’s Winter Outlook. 2022. SIPA Center on Global Energy Policy. https://www.energypolicy.columbia.edu/europe-s-winter-outlook, accessed 07/11/2022.

    • Search Google Scholar
    • Export Citation
  • Dawn, S.Tiwari, P.K.Goswami, A.K. (2019): An Approach for Long Term Economic Operations of Competitive Power Market by Optimal Combined Scheduling of Wind Turbines and FACTS Controllers Energy 181(C): 709723.

    • Search Google Scholar
    • Export Citation
  • Energeia (2022a): Energy Monitoring. https://www.energeia.in/ems, accessed 08/11/2022.

  • Energeia (2022b): Vision. https://www.energeia.in/vision, accessed 08/11/2022.

  • EUR-Lex (2022): Digitalising the Energy System - EU Action Plan. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52022DC0552&qid=1666369684560. 30/10/2022.

    • Search Google Scholar
    • Export Citation
  • Exeri (2022): Smart Grid Surveillance TM. https://www.exeri.se/smart-grid-surveillance#sgs/the-technology, accessed 08/11/2022.

  • Fraunhofer Institute for Industrial Mathematics (2022): EnerQuant: Quantum Computing for the Power Industry. Fraunhofer ITWM. https://www.itwm.fraunhofer.de/en/departments/fm/power-industry/enerquant-quantencomputing-power-industry.html, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • Frendo, O.Graf, J.Gaertner, N.Stuckenschmidt, H. (2020): Data-driven Smart Charging for Heterogeneous Electric Vehicle Fleets. Energy and AI 1: 100007.

    • Search Google Scholar
    • Export Citation
  • Haider, H. T.See, O. HElmenreich, W. (2016): A Review of Residential Demand Response of Smart Grid. Renewable Sustainable Energy Review 59: 166178.

    • Search Google Scholar
    • Export Citation
  • Head, B.Alford, J. (2013): Wicked Problems: Implications for Public Policy and Management. Administration & Society 47(6): 711739.

    • Search Google Scholar
    • Export Citation
  • Hou, W.Guo, L.Ning, Z. (2019): Local Electricity Storage for Blockchain-Based Energy Trading in Industrial Internet of Things. IEEE Transactions on Industrial Informatics 15: 36103619.

    • Search Google Scholar
    • Export Citation
  • INERATEC (2022): Processes. https://ineratec.de, accessed 08/11/2022.

  • Iota Communications (2022): Provider of Internet of Things Connectivity and Energy Solutions. https://www.iotacommunications.com, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • IRENA (2019): Artificial Intelligence and Big Data: Innovation Landscape Brief. International Renewable Energy Agency. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Sep/IRENA_AI_Big_Data_2019.pdf?, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • Italgas (2022): Italgas Innovation: Digital Transformation. https://www.italgas.it/en/group/innovation-digital-transformation/, accessed 08/11/2022.

    • Search Google Scholar
    • Export Citation
  • JoS QUANTUM (2022a): Quantum Computing for Capital and Energy Markets. https://jos-quantum.de/, accessed 08/11/2022.

  • JoS QUANTUM (2022b): Data Science with Advanced Technologies. https://jos-quantum.de/, accessed 08/11/2022.

  • Lempert, R.Popper, S.Bankes, S. (2002): Confronting Surprise. Social Science Computer Review (20): 420440.

  • Levi-Jakšić, M.Rakićević, J.Jovanović, M. (2018): Sustainable Technology and Business Innovation Framework: A Comprehensive Approach. Amfiteatru Economic Journal 20(48): 418436.

    • Search Google Scholar
    • Export Citation
  • Li, F. G.Bataille, C.Pye, S.O'Sullivan, A. (2019): Prospects for Energy Economy Modelling with Big Data: Hype, Eliminating Blind Spots, or Revolutionising the State of the Art? Applied Energy 239: 9911002.

    • Search Google Scholar
    • Export Citation
  • Mamun, A. A.Sohel, M.Mohammad, N.Haque Sunny, M. S.Dipta, D. R.Hossain, E. (2020): A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models. IEEE Access 8: 134911134939.

    • Search Google Scholar
    • Export Citation
  • Mathew, P. A.Dunn, L. N.Sohn, M. D.Mercado, A.Custudio, C.Walter, T. (2015): Big-data for Building Energy Performance: Lessons from Assembling a Very Large National Database of Building Energy Use. Applied Energy 140(C): 8593.

    • Search Google Scholar
    • Export Citation
  • Mbuwir, B. V.Geysen, D.Spiessens, F.Deconinck, G. (2020): Reinforcement Learning for Control of Flexibility Providers in a Residential Microgrid. IET Smart Grid 3(1): 98107.

    • Search Google Scholar
    • Export Citation
  • Navarra, D. (2022a): Strategic Renewable Energy and Power-To-X Projects: A Review of Best Practices, Risks and Challenges for Integrating Artificial Intelligence and Smart Sustainable Technologies. First Central European Power-to-Gas Conference: Regional Perspectives of Power-to-Methane and Carbon Capture Technologies. Hungarian Academy of Sciences, Budapest, Hungary, 12–13 December.

    • Search Google Scholar
    • Export Citation
  • Navarra, D. (2022b): Sustainable Technology, AI and the Localisation of SGDs: A Research Perspective on Smart Agriculture and the Insurance Industry in Africa. Panel on Improving Policy Analysis and Performance Governance through Outcome Oriented Approaches to “localize” the SDG Agenda, Asian Association for Public Administration (AAPA) Conference, Shanghai, China, December 3–4.

    • Search Google Scholar
    • Export Citation
  • Nielsen, J. J.Ganem, H.Jorguseski, L.Alic, K.Smolnikar, M.Zhu, Z.Pratas, N. K.Golinski, M.Zhang, H.Kuhar, U. (2017): Secure Real-Time Monitoring and Management of Smart Distribution Grid Using Shared Cellular Networks. IEEE Wireless Communications 24: 1017.

    • Search Google Scholar
    • Export Citation
  • Niet, I.van Est. R.Veraart, F. (2021): Governing AI in Electricity Systems: Reflections on the EU Artificial Intelligence Bill. Frontiers in Artificial Intelligence 4(July): 17.

    • Search Google Scholar
    • Export Citation
  • NODA (2022): Case Studies. https://noda.se/casestudies/, accessed 19/11/2022.

  • Onile, A. E.Machlev, R.Petlenkov, E.Levron, Y.Belikov, J. (2021): Uses of the Digital Twins Concept for Energy Services Intelligent Recommendation Systems, and Demand Side Management: A Review. Energy Reports 7: 9971015.

    • Search Google Scholar
    • Export Citation
  • Ouahiba, T.Fatima, B.Thafath, H. (2018): Smart Buildings and Occupants Satisfaction: The Case of Cyber Park of Sidi Abdallâh and Some Residential Buildings in Algeria. In: Hatti, M. (ed.) Artificial Intelligence in Renewable Energetic Systems. Lecture Notes in Networks and Systems. Cham: Springer.

    • Search Google Scholar
    • Export Citation
  • Pan, J.Jain R.Paul S.Vu T.Saifullah A.Sha M. (2015): An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments. IEEE Internet of Things Journal 2(6): 527537.

    • Search Google Scholar
    • Export Citation
  • Park, D. C.El-Sharkawi, M. A.Marks, R. J.Atlas, L. E.Damborg, M. J. (1991): Electric Load Forecasting Using an Artificial Neural Network. IEEE Transactions on Power Systems 6(2): 442449.

    • Search Google Scholar
    • Export Citation
  • Pearce, W.Holmberg, K.Hellsten, I.Nerlich, B. (2014): Climate Change on Twitter: Topics, Communities and Conversations about the 2013 IPCC Working Group 1 Report. Plos One 9(4): 111.

    • Search Google Scholar
    • Export Citation
  • Pfeifer, P.Schmidt, S.Betzner, F.Kollmann, M.Loewert, M.Böltken, T.Piermartini, P. (2022): Scale-up of Microstructured Fischer–Tropsch Reactors – Status and Perspectives. Current Opinion in Chemical Engineering 36: 100776.

    • Search Google Scholar
    • Export Citation
  • QC Ware (2022): QC Ware Solutions. https://www.qcware.com/solutions, accessed 19/11/2022.

  • Qiao, W.Yang, Z. (2020): Forecast the Electricity Price of U.S. Using a Wavelet Transform-Based Hybrid Model. Energy 193: 116704.

  • Rani, P.Mishra, A. R.Saha, A.Pamucar, D. (2021): Pythagorean Fuzzy Weighted Discrimination-Based Approximation Approach to the Assessment of Sustainable Bioenergy Technologies for Agricultural Residues. International Journal of Intelligent Systems 36(6): 29642990.

    • Search Google Scholar
    • Export Citation
  • Reijnders, V. M. J. J.van der Laan, M. D.Dijkstra, R.Sioshansi, F. P. (2020): Energy Communities: a Dutch Case Study. In: Sioshansi, F. P. (ed.): Behind and beyond the Meter. Digitalization, Aggregation, Optimization .San Francisco: Academic Press.

    • Search Google Scholar
    • Export Citation
  • Sánchez, M.Prado-Gonjal, B.Ávila-Brande, J.Chen, D.Morán, M.Viola, E. B. (2014): High Performance La0.3Ca0.7Cr0.3Fe0.7O3-D Air Electrode for Reversible Solid Oxide Fuel Cell Applications. International Journal of Hydrogen Energy 40(4): 19021910.

    • Search Google Scholar
    • Export Citation
  • Schuelke-Leech, B. A.Barry, B.Muratori, M.Yurkovich, B. J. (2015): Big Data Issues and Opportunities for Electric Utilities. Renewable Sustainable Energy Review 52(C): 937947.

    • Search Google Scholar
    • Export Citation
  • SeeO2 Energy (2022): What We Do. https://www.seeo2energy.com/what-we-do/, accessed 19/11/2022.

  • Shafiei, M. W.Abadi, H. (2017): The Importance of Green Technologies and Energy Efficiency for Environmental Protection. International Journal of Applied Environmental Sciences 12(5): 937951.

    • Search Google Scholar
    • Export Citation
  • Shahrokni, H.Levihn, F.Brandt, N. (2014): Big Meter Data Analysis of the Energy Efficiency Potential in Stockholm’s Building Stock. Energy Build 78: 153164.

    • Search Google Scholar
    • Export Citation
  • Shen, J.Zhou, T.Wei, F.Sun, X.Xiang, Y. (2018): Privacy-preserving and Lightweight Key Agreement Protocol for V2G in the Social Internet of Things. IEEE Internet Things Journal 5(4): 25262536.

    • Search Google Scholar
    • Export Citation
  • Smart City Sweden (2022): The Intelligent District Heating System – Makes Use of AI. Best practice - Smart City Sweden. https://smartcitysweden.com/best-practice/430/the-intelligent-district-heating-system-makes-use-of-ai/, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • Solyali, D. (2020): A Comparative Analysis of Machine Learning Approaches for Short-/long-Term Electricity Load Forecasting in Cyprus. Sustainability 12(9): 3612.

    • Search Google Scholar
    • Export Citation
  • StartUs Insights (2022a): Explore 2+ Mio. Startups & Scaleups: StartUs Insights Discovery Platform. https://www.startus-insights.com/startus-insights-platform/, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • StartUs Insights (2022b): Heat Map: 5 Top Carbon Capture & Storage Startups. https://www.startus-insights.com/wp-content/uploads/2020/06/Carbon-Capture-_-Storage-Startups-Energy-Heat-Map-StartUs-Insights-noresize.png, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • StartUs Insights (2022c): Top 10 Energy Industry Trends & Innovations. https://www.startus-insights.com/innovators-guide/top-10-energy-industry-trends-innovations-in-2021/, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • Stirling, A. (2007): Deliberate Futures: Precaution and Progress in Social Choice of Sustainable Technology. Sustainable Development 15(5): 286295.

    • Search Google Scholar
    • Export Citation
  • STOREH (2022): Hydrogen on Demand, Energy Storage Device. https://store-h.com/, accessed 19/11/2022.

  • Stritch, J. M.Darnall, N.Hsueh, L.Bretschneider, S. (2018): Green Technology Firms and Sustainable Public Purchasing. IEEE Engineering Management Review 46(1): 128131.

    • Search Google Scholar
    • Export Citation
  • Subramaniam, M. (2022): The Future of Competitive Strategy: Unleashing the Power of Data and Digital Ecosystems. The MIT Press.

  • US Department of Energy (2022): Cybersecurity Considerations for Distributed Energy Resources on the U.S. Electric Grid .https://www.energy.gov/sites/default/files/2022-10/Cybersecurity%20Considerations%20for%20Distributed%20Energy%20Resources%20on%20the%20U.S.%20Electric%20Grid.pdf, accessed 19/11/2022.

    • Search Google Scholar
    • Export Citation
  • Wang, S.Zhang, Q.Martin, R. V.Philip, S.Liu, F.M. Li (2015): Satellite Measurements Oversee China’s Sulfur Dioxide Emission Reductions from Coal-Fired Power Plants. Environmental Research Letters 10(11): 114015.

    • Search Google Scholar
    • Export Citation
  • Weiss, M.Helfenstein, A.Mattern, F.Staake, T. (2012): Leveraging Smart Meter Data to Re- Cognize Home Appliances. 2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, Switzerland.

    • Search Google Scholar
    • Export Citation
  • Wulf, C.Zapp, P.Schreiber, A. (2020): Review of Power-To-X Demonstration Projects in Europe. Frontiers in Energy Research 8: 112.

    • Search Google Scholar
    • Export Citation
  • Xu, Y.Ahokangas, P.Louis, J. N.Pongrácz, E. (2019): Electricity Market Empowered by Artificial Intelligence: A Platform Approach. Energies 12(21): 4128.

    • Search Google Scholar
    • Export Citation
  • Zahmatkesh H.Al-Turjman F. (2020): Fog Computing for Sustainable Smart Cities in the IoT Era: Caching Techniques and Enabling Technologies - an Overview. Sustainable Cities and Society 59: 102139.

    • Search Google Scholar
    • Export Citation
  • Zhou, Y.Zhai, Q.Zhou, M.Li, X. (2020): Generation Scheduling of Self-Generation Power Plant in Enterprise Microgrid with Wind Power and Gateway Power Bound Limits, IEEE Transactions on Sustainable Energy 11(2020): 758770.

    • Search Google Scholar
    • Export Citation
  • Ziyi, L.Xinyi, Y.Yingxue, W.Weidi, L.Siliang, L.Yuankun, Z.Zihan, H.Hong, Z.Shuming, D.Jie, X.Jiachen, J.Kui X.Xiaowang, Z.Wenbin, H.Yida, D. (2020): A Survey of Artificial Intelligence Techniques Applied in Energy Storage Materials R&D. Frontiers in Energy Research 8: 116.

    • Search Google Scholar
    • Export Citation
  • Zor, K.Timur, O.Teke, A. (2017): A State-Of-The-Art Review of Artificial Intelligence Techniques for Short-Term Electric Load Forecasting. 6th International Youth Conference on Energy. Budapest, Hungary.

    • Search Google Scholar
    • Export Citation
  • Interview#1: Amos Agbetile, Electrical Engineer, 21 April 2023, online.

  • Interview#2: Professor Pierluigi Siano, Scientific Director of the Smart Grids and Smart Cities Laboratory, Department of Management & Innovation Systems, University of Salerno, 27 April 2023, online.

    • Search Google Scholar
    • Export Citation
  • Interview#3: Richard Gardner, CEO, Modulus, 27 April 2023, online.

  • Interview#4: Dr Eva-Marie Muller-Stuler, Partner EY Data & Analytics, 28 April 2023, online.

  • Interview#5: Dr Roger Miles, Oxford Scholar, 4 May 2023, online.

  • Interview#6: Dr Ansgar Koene, Global AI Ethics and Regulatory Leader at EY, 5 May 2023, online.

  • Interview#7: Vice President, Data Science and Artificial Intelligence, anonymous company, 8 May 2023, online.

  • Interview#8: Head of Artificial Intelligence, Anonymous Company, 10 May 2023, online.

  • Interview#9: Dr Bruno Zarpelao, Associate Professor at State University of Londrina (Brazil), 11 May 2023, online.

  • Interview#10: Maria Axente, Responsible AI Executive, PwC, 1 June 2023, online.

  • Collapse
  • Expand
The author instruction is available in PDF.
Please, download the file from HERE.
 
The Open Access statement together with the description of the Copyright and License Policy are available in PDF.
Please, download the file from HERE.

Editor-in-chief: Balázs SZENT-IVÁNYI

Co-Editors:

  • Péter MARTON (Corvinus University, Budapest)
  • István KÓNYA (Corvinus University, Budapest)
  • László SAJTOS (The University of Auckland)
  • Gábor VIRÁG (University of Toronto)

Associate Editors:

  • Tamás BOKOR (Corvinus University, Budapest)
  • Sándor BOZÓKI (Corvinus University Budapest)
  • Bronwyn HOWELL (Victoria University of Wellington)
  • Hintea CALIN (Babeş-Bolyai University)
  • Christian EWERHART (University of Zürich)
  • Clemens PUPPE (Karlsruhe Institute of Technology)
  • Zsolt DARVAS (Bruegel)
  • Szabina FODOR (Corvinus University Budapest)
  • Sándor GALLAI (Corvinus University Budapest)
  • László GULÁCSI (Óbuda University)
  • Dóra GYŐRFFY (Corvinus University Budapest)
  • György HAJNAL (Corvinus University Budapest)
  • Krisztina KOLOS (Corvinus University Budapest)
  • Alexandra KÖVES (Corvinus University Budapest)
  • Lacina LUBOR (Mendel University in Brno)
  • Péter MEDVEGYEV (Corvinus University Budapest)
  • Miroslava RAJČÁNIOVÁ (Slovak University of Agriculture)
  • Ariel MITEV (Corvinus University Budapest)
  • Éva PERPÉK (Corvinus University Budapest)
  • Petrus H. POTGIETER (University of South Africa)
  • Sergei IZMALKOV (MIT Economics)
  • Anita SZŰCS (Corvinus University Budapest)
  • László TRAUTMANN (Corvinus University Budapest)
  • Trenton G. SMITH (University of Otago)
  • György WALTER (Corvinus University Budapest)
  • Zoltán CSEDŐ (Corvinus University Budapest)
  • Zoltán LŐRINCZI (Ministry of Human Capacities)

Society and Economy
Institute: Corvinus University of Budapest
Address: Fővám tér 8. H-1093 Budapest, Hungary
Phone: (36 1) 482 5406
E-mail: balazs.szentivanyi@uni-corvinus.hu

Indexing and Abstracting Services:

  • CABELLS Journalytics
  • DOAJ
  • International Bibliographies IBZ and IBR
  • International Political Science Abstracts
  • JSTOR
  • SCOPUS
  • RePEc
  • Referativnyi Zhurnal

 

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed
not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed
not indexed

Scimago  
Scimago
H-index
15
Scimago
Journal Rank
0.217
Scimago Quartile Score

Business and International Management Q3
Economics, Econometrics and Finance (miscellaneous) Q3
Industrial Relations Q3
Public Administration Q3
Sociology and Political Science Q3
Strategy and Management Q4

Scopus  
Scopus
Cite Score
1.5
Scopus
Cite Score Rank
Sociology and Political Science 602/1415 (57th PCTL)
General Economics, Econometrics and Finance 131/279 (53rd PCTL)
Industrial Relations 31/57 (46th PCTL)
Public Administration 3126/213 (41th PCTL)
Business and International Management 302/436 (30th PCTL)
Strategy and Management 343/473 (27th PCTL)
Scopus
SNIP
0.468

 

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
13
Scimago
Journal Rank
0,196
Scimago Quartile Score Economics, Econometrics and Finance (miscellaneous) (Q3)
Industrial Relations (Q3)
Sociology and Political Science (Q3)
Business and International Management (Q4)
Public Administration (Q4)
Strategy and Management (Q4)
Scopus  
Scopus
Cite Score
1,2
Scopus
CIte Score Rank
Sociology and Political Science 626/1345 (Q2)
General Economics, Econometrics and Finance 131/260 (Q3)
Industrial Relations 35/57 (Q3)
Public Administration 120/190 (Q3)
Business and International Management 292/423 (Q3)
Strategy and Management 340/456 (Q3)
Scopus
SNIP
0,270

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,157
Scimago
Quartile Score
Business and International Management Q4
Economics, Econometrics and Finance (miscellaneous) Q4
Industrial Relations Q4
Public Administration Q4
Sociology and Political Science Q3
Strategy and Management Q4
Scopus
Cite Score
103/117=0,9
Scopus
Cite Score Rank
Business and International Management 305/399 (Q4)
General Economics, Econometrics and Finance 137/243 (Q3)
Industrial Relations 40/54 (Q3)
Public Administration 116/165 (Q3)
Sociology and Political Science 665/1269 (Q3)
Strategy and Management 351/440 (Q4)
Scopus
SNIP
0,171
Scopus
Cites
157
Scopus
Documents
24
Days from submission to acceptance 148
Days from acceptance to publication 50

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,228
Scimago
Quartile Score
Business and International Management Q3
Economics, Econometrics and Finance (miscellaneous) Q3
Industrial Relations Q3
Public Administration Q3
Sociology and Political Science Q3
Strategy and Management Q3
Scopus
Cite Score
87/110=0,8
Scopus
Cite Score Rank
Business and International Management 286/394 (Q3)
General Economics, Econometrics and Finance 125/228 (Q3)
Industrial Relations 38/58 (Q3)
Public Administration 114/157 (Q3)
Sociology and Political Science 645/1243 (Q3)
Strategy and Management 330/427 (Q4)
Scopus
SNIP
0,308
Scopus
Cites
132
Scopus
Documents
22

 

Society and Economy
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 900 EUR/article with enough waivers
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Sufficient number of full waiver available. Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

Society and Economy
Language English
Size B5
Year of
Foundation
1972
Volumes
per Year
1
Issues
per Year
4
Founder Budapesti Corvinus Egyetem
Founder's
Address
H-1093 Budapest, Hungary Fővám tér 8.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1588-9726 (Print)
ISSN 1588-970X (Online)