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An electricity substitution strategy that replaces fossil fuels such as coal and oil with electricity in end-use energy consumption, can effectively contribute to an energy transition and the early achievement of carbon peaking and carbon neutrality targets. As the benefits of electricity substitution are not synchronized across China's regions, this paper uses a three-stage data envelopment analysis (DEA) model to measure the efficiency of electric energy substitution in 30 provinces of China in 2017. The results show that both environmental factors and random errors have significant effects on energy efficiency. After eliminating these influences, the efficiency of electrical energy substitution among regions presented the following pattern: "high in the east and low in the west". According to the evaluation results, this paper proposes corresponding suggestions for the development of electrical energy substitution. [ABSTRACT FROM AUTHOR]

Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new electrical demand sectors, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionary new power grid system, called smart grid, has been developed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since conventional distribution networks lack smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of the electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG. [ABSTRACT FROM AUTHOR]

This paper develops a discrete operation optimization model for combined heat and powers (CHPs) in deregulated energy markets to maximize owners' profits, where energy price forecasting is included. First, a single input and multi-output (SIMO) model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at different loading levels. Then, the energy prices are forecasted with a gray forecasting model and revised in real-time based on the actual prices by using the least squares method. At last, a discrete optimization model and corresponding dynamic programming algorithm are developed to design the optimal operation strategies for CHPs in real-time. Based on the forecasted prices, the potential operating strategy which may produce the maximum profits is pre-developed. Dynamic modification is then conducted to adjust the pre-developed operating strategy after the actual prices are known. The proposed method is implemented on a 1 MW CHP on a typical day. Results show the optimized profits comply well with those derived from real-time prices after considering dynamic modification process. [ABSTRACT FROM AUTHOR]

Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform. [ABSTRACT FROM AUTHOR]

This study integrates genetic algorithms with simulation programs, applying the genetic algorithm's (GA) fitness calculation within the simulation to reduce complexity and significantly improve the efficiency of the optimization process. Additionally, the simulation introduces the concept of "Field Leveling" (FL), utilizing a push–pull strategy to explore more space for absorbing and utilizing unnecessary spillage for energy generation, thereby maximizing electricity production and ensuring optimal reservoir scheduling. Two methods are provided, namely the field-leveling genetic algorithms GAFL1 and GAFL2. GAFL1 involves only pushing and does not include a push–pull process; thus, it cannot optimize spillage. On the other hand, GAFL2 implements a complete push–pull strategy, continuously exploring additional space to absorb and utilize unnecessary spillage. Both GAFL1 and GAFL2 achieved reasonable results; specifically, compared to SQP, GAFL1 improved firm yield by 8.3%, spillage increased by 2.2 times, and total energy decreased by 1.2%. GAFL2, building on the basis of GAFL1, effectively reduces spillage under all hydrological conditions without affecting the highest priority of stable output. However, the impact of reducing spillage on energy generation is not consistent; in wet and dry years, reducing spillage increases energy generation. However, in normal years, a reduction in spillage corresponds with decreased energy generation. [ABSTRACT FROM AUTHOR]

Energy storage technologies have been rapidly evolving in recent years. Energy storage plays different roles in various scenarios. For electricity consumers, they are concerned with how to use the energy storage system (ESS) to reduce their costs of electricity or increase their profits. In this paper, a stochastic optimization method for energy storage sizing based on an expected value model for consumers with Photovoltaic Generation (PV) is proposed. Firstly, the Gaussian mixture model clustering method is used to cluster the historical load and PV data and calculate the probability of each cluster. Secondly, the optimal model of total system profit is established. Finally, according to the expected value model, the optimal ESS power and capacity are determined. Two case studies are used to demonstrate the calculation of optimal ESS capacity. The results obtained by the method proposed in this paper are compared with the results produced by the deterministic method. Through the analysis and comparison, the validity and superiority of the method proposed in this paper are verified. The profits obtained by the method proposed in this paper are 0.87% to 127.16% more than the deterministic method. [ABSTRACT FROM AUTHOR]

This paper deals with energy efficiency examined through an integrated model that links energy with environment, technology, and urbanisation as related areas. Our main goal is to discover how efficiently developed countries use primary energy and electricity (secondary energy). We additionally want to find out how the inclusion of environmental care and renewable energy capacity affects efficiency. For that purpose, we set up an output-oriented BCC data envelopment analysis that employs a set of input variables with non-negative values to calculate the efficiency scores on minimising energy use and losses as well as environmental emissions for a sample of 30 OECD member states during the period from 2001 to 2018. We develop a couple of baseline models in which we find that countries have mean inefficiency margins of 16.1% for primary energy and from 10.8 to 13.5% for electricity. The results from the extended models show that taking care about environment does not affect efficiency in general, while the reliance on energy produced from renewable sources does slightly reduce it. [ABSTRACT FROM AUTHOR]

The design of electricity markets determines the technologies, services and modes of operation that can access value, consequently shaping current and future electricity landscapes. This paper highlights that the efficacy of Great Britain's electricity market design in facilitating net zero is inadequate and must be reconfigured. The rules of the current electricity market design are remnants of an electricity sector dominated by large-scale, centralised, fossil fuel technologies. Therefore, routes to market for the provision of necessary services to support net zero, not least flexibility, are largely inaccessible for distributed energy resources and, despite their benefits to the system, are thus undervalued. Based upon a review and consolidation of 30 proposed electricity market designs from liberalised electricity sectors, this paper proposes a new electricity market design for Great Britain. This design is presented alongside a new institutional framework to aid in the efficient operation of the market. Specifically, this paper proposes a new local balancing and coordinating market located at each grid supply point (the transmission and distribution interface). This is realised through the implementation of a distributed locational marginal pricing structure which is governed by the evolution of the current distributed network operator, known as the distributed service provider (DSP). The DSP also operates a local balancing and ancillary market for their geographical area. The wholesale market is reconfigured to coordinate with these new local markets and to harmonise the actors across the distribution and transmission network. [ABSTRACT FROM AUTHOR]

Electricity consumption in Europe is constantly increasing, despite the fact that in recent years, huge efforts in terms of programs and regulations have been made towards energy demand reduction and energy systems improvement. Since the electricity demand affects both the operation of the supply and distribution plants and the thermal loads of buildings, the importance of providing a proper definition of demand profiles is evident. The main aim of the paper is to provide a set of standard electricity profiles that can reasonably be adopted as input in energy simulations related to the built environment, with particular regards to the Italian context. The work presented in this paper originated within a wider long lasting research aimed at developing a platform for buildings' energy simulations at district level, with particular reference to the Italian conditions. In this context, it was necessary to define hourly profiles regarding both occupancy and electricity use for lighting and appliances related to different building uses and typologies. For this purpose, the main methods and references for defining electricity loads in buildings were evaluated and average hourly profiles were accordingly developed for residential and commercial buildings. Then the related internal gains were determined and compared to the current Italian standards. [ABSTRACT FROM AUTHOR]

This paper discusses the authors' experience gained with the selection of Projects of Common Interest (PCIs) in the thematic area of smart grids deployment, in the context of the TEN-E Regulation. It presents the framework for assessing candidate electricity smart grids for inclusion in the European Union list of PCIs, in view of the TEN-E Regulation and the existing literature on assessment methodologies for energy infrastructure projects. It also provides an overview of smart grid projects included in the PCI lists, with the aim to shed light on the types of projects and their contribution to accelerating the development of European cross-border energy infrastructure projects to respond to EU energy and climate targets. The paper concludes with discussion of recent regulatory initiatives and their potential implications on the presented methodology. [ABSTRACT FROM AUTHOR]

Controlling the cost of electricity consumption remains a major concern, particularly in the residential sector. Smart home electricity management systems (HEMS) are becoming increasingly popular for providing uninterrupted power and improved power quality, as well as for reducing the cost of electricity consumption. When power transfer is required between a storage system and the AC grid, and vice versa, these HEMS require the use of a bidirectional DC–AC converter. This paper emphasizes the potential value of an almost unexplored topology, the design of which was based on the generation of sinusoidal signals from sinusoidal half waves. A DC–DC stage, which behaved as a configurable voltage source, was in series with a DC–AC stage, i.e., an H-bridge, to achieve an architecture that could operate in both grid and off-grid configurations. Wide bandgap power switches (silicon carbide metal-oxide-semiconductor field-effect transistors [MOSFETs]), combined with appropriate control strategies, were the keys to increasing compactness of the converter while ensuring good performance, especially in terms of efficiency. The converter was configured to automatically change the operating mode, i.e., inverter or rectifier in power factor correction mode, according to an instruction issued by the HEMS; the latter being integrated in the control circuit with automatic duty cycle management. Therefore, the HEMS set the amount of energy to be injected into the grid or to be stored. The experimental results validate the operating modes of the proposed converter and demonstrate the relevance of such a topology when combined with an HEMS, especially in the case of an AC grid connection. The efficiency measurements of the bidirectional DC–AC converter, performed in grid-connected inverter mode, show that we exceeded the efficiency target of 95% over the entire output power range studied, i.e., from 100 W to 1.5 kW. [ABSTRACT FROM AUTHOR]

The wholesale electricity and capacity market constitute the backbone of the Russian power industry. It is in this market that large suppliers and buyers operate, and its entire turnover is consequently transmitted to the retail market. Our paper presents a theoretical overview of the main tools for forming the cost of electricity and capacity in the wholesale market in Russia (depending on the regional affiliation), the type of end users, and the degree of state participation. We consider the specifics of the formation of the cost of electricity and capacity in the price and non-price wholesale markets of Russia, which differ in territorial, climatic, and economic characteristics, as well as the established structure of generation. In the empirical part of the paper, we carry out a structural analysis of the volumes of trade in electricity and capacity in the price and non-price zones of the market. Furthermore, we explain the reasons for the current dynamics of prices in the wholesale market. Using the obtained results, we calculate the maximum annual effect of the solar power plant operation in various zones of the Russian wholesale market, as well as in the retail market. In addition, we estimate the economic incentive for the transition of the functioning of power facilities from the wholesale to the retail market. Our results can be of considerable practical importance and might be used for improving the strategy for the development of the electric power industry at the regional level both in Russia and in the other countries. [ABSTRACT FROM AUTHOR]

New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark's Machine Learning Library. The results, using electricity consumption for the years 2011-2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus. [ABSTRACT FROM AUTHOR]

The exploitation of technologies with which to harness the energy from ocean currents will have considerable possibilities in the future thanks to their enormous potential for electricity production and their high predictability. In this respect, the development of methodologies for the economic viability of these technologies is fundamental to the attainment of a consistent quantification of their costs and the discovery of their economic viability, while simultaneously attracting investment in these technologies. This paper presents a methodology with which to determine the economic viability of tidal energy projects, which includes a technical study of the life-cycle costs into which the development of a tidal farm can be decomposed: concept and definition, design and development, manufacturing, installation, operation and maintenance and dismantling. These cost structures are additionally subdivided by considering their sub-costs and bearing in mind the main components of the tidal farm: the nacelle, the supporting tidal energy converter structure and the export power system. Furthermore, a technical study is developed in order to obtain an estimation of the annual energy produced (and, consequently, the incomes generated if the electric tariff is known) by considering its principal attributes: the characteristics of the current, the ability of the device to capture energy and its ability to convert and export the energy. The methodology has been applied (together with a sensibility analysis) to the particular case of a farm composed of first generation tidal energy converters in one of the Channel Island Races, the Alderney Race, in the U.K., and the results have been attained by means of the computation of engineering indexes, such as the net present value, the internal rate of return, the discounted payback period and the levelized cost of energy, which indicate that the proposed project is economically viable for all the case studies. [ABSTRACT FROM AUTHOR]

The transition towards energy efficiency measures and green energy sources is strongly fostered by the European Union. Italy is among the EU countries that have heavily invested in renewable energy sources, more than doubling their share in gross final energy consumption. In particular, biogas has a pivotal role in the generation of electricity and can also be upgraded into biomethane, with a higher and more stable energy content. In this study, the sustainability of the supply chain of electricity from biogas in Italy has been thoroughly analyzed in the broader framework of the ARCADIA (Life Cycle Approach in Public Procurement and Italian LCA Database for Resource Efficiency) project. The environmental assessment, carried out by means of Life Cycle Assessment (LCA), provides a two-fold perspective. Firstly, it allows us to identify the main hotspots of the investigated system, such as the cultivation of dedicated crops, and to provide useful insights for improving environmental performance. Furthermore, a focus on the modeling of the dataset related to the production of electricity from biogas within the Italian electricity mix represents a step ahead in the LCA research, filling the lack of site-specific databases for reliable LCA results. [ABSTRACT FROM AUTHOR]

The need for renewable energy is regarded as a major component in the move towards achieving sustainable development. Using a large sample of 177 countries over the period 1990 to 2020, this research explores the impact of the most significant drivers of renewable energy. Findings from this work contribute to the literature by identifying the most significant drivers of renewable energy deployment and their different responses in developed and developing economies. Empirical results suggest that GDP, oil price, access to electricity, and CO2 and methane emissions are significant determinants of renewables both as a share in energy consumption and as a share in electricity production. Additionally, trade is found to be a significant driver for the share of renewables in total energy consumption but not for the share of renewables in the electricity production model. Finally, our findings indicate that the factors influencing the development of renewables vary significantly between developed and developing countries, necessitating distinct approaches for each group of countries. These results can play a significant role from a policy perspective in designing and implementing specific policies to increase renewable energy deployment. [ABSTRACT FROM AUTHOR]

The research work explores the impact of temperature on Silicon photovoltaic (PV) panels, considering Nigeria as a case study. It is found that high solar radiation in Nigeria increases the surface temperature of PV panels above 25 °C of the optimal operating temperature. The redundant energy gain from solar irradiance creates heat at the rear of solar panels and reduces their efficiency. Cooling mechanisms are therefore needed to increase efficiency. In this study, we demonstrated a unique hybrid system design employing a heat exchanger at the back of the panel, with water circulated through the back of the PV panel to cool the system. The system was simulated using TRNSYS at three locations in Nigeria—Maiduguri, Makurdi, and Port Harcourt. The results of the peak annual electrical power output in Maiduguri give a power yield of 1907 kWh/kWp, which is the highest, due to a high solar radiation average of 727 W/m2 across the year. For Makurdi, the peak annual electrical power output is 1542 kWh/kWp, while for Port Harcourt the peak power output is 1355 kWh/kWp. It was observed that the surface temperature of Polycrystalline Si-PV was decreased from 49.25 °C to 38.38 °C. The electrical power was increased from 1526.83 W to 1566.82 W in a day, and efficiency increased from 13.99% to 15.01%. [ABSTRACT FROM AUTHOR]

This study develops a new method to evaluate the economic viability of co-generation electric vehicle stations that concurrently generate electricity and hydrogen for charging battery electric vehicles and refueling hydrogen vehicles. The approach uniquely differentiates the costs associated with various energy outputs in co-generation stations and includes often-overlooked peripheral devices critical for accurate evaluation of the levelized cost of electricity (LCOE) and hydrogen (LCOH). The method was tested across three design configurations: two featuring single storage options (battery and fuel cell, respectively) and a third using hybrid storage employing both. Each configuration was modeled, simulated, and optimized using HOMER Pro 3.14.2 to determine the most optimal sizing solution. Then, based on the optimal sizing of each design, LCOE and LCOH were evaluated using the proposed method in this study. The analysis revealed that excluding often-overlooked peripheral devices could lead to a 27.7% error in LCOH evaluation, while the impact on LCOE was less than 1%. Among different configurations, the design with hybrid storage proved economically superior, achieving a total levelized cost of energy (TLCOE) for the entire system of USD 0.113/kWh, with the LCOE at USD 0.025/kWh and LCOH at USD 0.088/kWh (or USD 3.46/kg). Comparative analysis with state-of-the-art studies confirmed the accuracy of the proposed method. This study provides a more precise and holistic approach that can be leveraged for the feasibility analysis of electric vehicle stations globally, enhancing strategic decision-making in sustainable energy planning. [ABSTRACT FROM AUTHOR]

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy's 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability. [ABSTRACT FROM AUTHOR]

The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of buildings is one of the main challenges facing Europe in the context of climate neutrality policy. In this article, the authors decided to investigate the impact of thermal insulation of building envelopes and the type of ventilation on the energy efficiency of multi-family buildings located in different Polish cities. In accordance with EU directives, economic analyses of the costs incurred during the lifecycle and calculations of C O 2   emissions were carried out for the analyzed facilities. It was determined what measures need to be taken under Polish climatic conditions in order to meet the requirements for improving the energy efficiency of multi-family buildings. The multi-criteria analyses presented in this article provide a voice in the discussion of the issues related to the achievement of the energy performance of buildings with almost zero primary energy demand (nZEB). Based on the conclusions of the research conducted, it was possible to develop general recommendations for investors and designers of multi-family residential buildings characterized by energy efficiency and reduced environmental impact. [ABSTRACT FROM AUTHOR]

In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach [ABSTRACT FROM AUTHOR]

The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model. [ABSTRACT FROM AUTHOR]

Energy storage will be required over a wide range of discharge durations in future zero-emission grids, from milliseconds to months. No single technology is well suited for the complete range. Using 9 years of UK data, this paper explores how to combine different energy storage technologies to minimize the total cost of electricity (TCoE) in a 100% renewable-based grid. Hydrogen, compressed air energy storage (CAES) and Li-ion batteries are considered short-, medium-, and long-duration energy stores, respectively. This paper analyzes different system configurations to find the one leading to the lowest overall cost. Results suggest that the UK will need a storage capacity of ~66.6 TWh to decarbonize its grid. This figure considers a mix of 85% wind + 15% solar-photovoltaics, and 15% over-generation. The optimum distribution of the storage capacity is: 55.3 TWh in hydrogen, 11.1 TWh in CAES and 168 GWh in Li-ion batteries. More than 60% of all energy emerging from storage comes from medium-duration stores. Based on current costs, the storage capacity required represents an investment of ~£172.6 billion, or approximately 8% of the country's GDP. With this optimum system configuration, a TCoE of ~75.6 £/MWh is attained. [ABSTRACT FROM AUTHOR]

This paper proposes a modification to the design of a standard PV module by enclosing the skeleton space and using forced ventilation. The purpose of this research was to develop a method for calculating the amount of heat gained during PV module cooling. A simplifying assumption was to omit the electrical energy consumed by the fans forcing the airflow. For testing at low irradiance, a prototype halogen radiation simulator of our own design was used, which is not a standardized radiation source used for testing PV modules. Two measurements were also made under natural, stable solar radiation. The modified PV module was tested for three ventilation rates and compared with the results obtained for the standard PV module. In all tested cases, an increase in electrical efficiency of about 2% was observed with increasing radiation intensity. The thermal efficiency decreased by about 5% in the analyzed cases and the highest value of 10.47% was obtained for the highest value of cooling airflow rate. In conclusion, the study results represent a certain compromise: an increase in electrical efficiency with a simultaneous decrease in thermal efficiency. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted electricity consumption patterns and such an impact cannot be analyzed by simple data analytics. In China, specifically, city lock-down policies lasted for only a few weeks and the spread of COVID-19 was quickly under control. This has made it challenging to analyze the hidden impact of COVID-19 on electricity consumption. This paper targets the electricity consumption of a group of regions in China and proposes a new clustering-based method to quantitatively investigate the impact of COVID-19 on the industrial-driven electricity consumption pattern. This method performs K-means clustering on time-series electricity consumption data of multiple regions and uses quantitative metrics, including clustering evaluation metrics and dynamic time warping, to quantify the impact and pattern changes. The proposed method is applied to the two-year daily electricity consumption data of 87 regions of Zhejiang province, China, and quantitively confirms COVID-19 has changed the electricity consumption pattern of Zhejiang in both the short-term and long-term. The time evolution of the pattern change is also revealed by the method, so the impact start and end time can be inferred. Results also show the short-term impact of COVID-19 is similar across different regions, while the long-term impact is not. In some regions, the pandemic only caused a time-shift in electricity consumption; but in others, the electricity consumption pattern has been permanently changed. The data-driven analysis of this paper can be the first step to fully interpret the COVID-19 impact by considering economic and social parameters in future studies. [ABSTRACT FROM AUTHOR]

Indonesia has an increasing electricity demand that is mostly met with fossil fuels. Although Indonesia plans to ramp up Renewable Energy Technologies (RET), implementation has been slow. This is unfortunate, as the RET potential in Indonesia might be higher than currently assumed given the archipelago's size. However, there is no literature overview of RET potentials in Indonesia and to what extent they can meet current and future electricity demand coverage. This paper reviews contemporary literature on the potential of nine RET in Indonesia and analyses their impact in terms of area and demand coverage. The study concludes that Indonesia hosts massive amounts of renewable energy resources on both land and sea. The potentials in the academic and industrial literature tend to be considerably larger than the ones from the Indonesian Energy Ministry on which current energy policies are based. Moreover, these potentials could enable a 100% renewables electricity system and meet future demand with limited impact on land availability. Nonetheless, the review showed that the research topic is still under-researched with three detected knowledge gaps, namely the lack of (i) economic RET potentials, (ii) research on the integrated spatial potential mapping of several RET and (iii) empirical data on natural resources. Lastly, this study provides research and policy recommendations to promote RET in Indonesia. [ABSTRACT FROM AUTHOR]

Optimal planning of a remote area electricity supply (RAES) system is a vital challenge to achieve a reliable, clean, and cost-effective system. Various components like diesel generators, renewable energy sources, and energy storage systems are used for RAES systems. Due to the different characteristics and economic features of each component, optimal planning of RAES systems is a challengeable task. This paper presents an overview of the optimal planning procedure for RAES systems by considering the important components, parameters, methods, and data. A timely review on the state of the art is presented and the applied objective functions, design constraints, system components, and optimization algorithms are specified for the existing studies. The existing challenges for RAES systems' planning are recognized and discussed. Recent trends and developments on the planning problem are explained in detail. Eventually, this review paper gives recommendations for future research to explore the optimal planning of components in RAES systems. [ABSTRACT FROM AUTHOR]

The main part of mankind's ecological footprint is the carbon footprint, a measure of the environmental impact of humanity's energy release from fossil fuels. The use of fossil fuels will have to change in the forthcoming decades to a largely climate-neutral use of solar energy enabled by dramatic cost reductions for PV and wind energy systems. The impact of this trend on world society has been discussed in a previous paper. In connection with these important technical developments, the role of electricity, its transport and storage will alter in the coming decades, allowing the design and use of larger and larger electricity grids and a parallel use of hydrogen for both storage and energy transport. This will further change the energy landscape of the world. All these developments and their relationship to global sustainable development are elaborated in this cross-disciplinary paper by specifically analyzing whether the Sustainable Development Goals by the United Nations are an effective road map for humanity to handle global climate change risks. [ABSTRACT FROM AUTHOR]

This paper presents the development and evaluation of EnnerSpectrum, a platform for electricity monitoring. The development was motivated by a gap between academic, fully custom-made monitoring solutions and commercial proprietary monitoring approaches. EnnerSpectrum is composed of two main entities, the back end, and the Gateway. The back end is a server comprised of flexible entities that can be configured to different monitoring scenarios. The Gateway interacts with equipment at a site that cannot interact directly with the back end. The paper presents the architecture and configuration of EnnerSpectrum for a long-term case study with 13 prosumers of electricity for approximately 36 months. During this period, the proposed system was able to adapt to several building and monitoring conditions while acquiring 95% of all the available consumption data. To finalize, the paper presents a set of lessons learned from running such a long-term study in the real world. [ABSTRACT FROM AUTHOR]

An important revenue stream for electric battery operators is often arbitraging the hourly price spreads in the day-ahead auction. The optimal approach to this is challenging if risk is a consideration as this requires the estimation of density functions. Since the hourly prices are not normal and not independent, creating spread densities from the difference of separately estimated price densities is generally intractable. Thus, forecasts of all intraday hourly spreads were directly specified as an upper triangular matrix containing densities. The model was a flexible four-parameter distribution used to produce dynamic parameter estimates conditional upon exogenous factors, most importantly wind, solar and the day-ahead demand forecasts. These forecasts supported the optimal daily scheduling of a storage facility, operating on single and multiple cycles per day. The optimization is innovative in its use of spread trades rather than hourly prices, which this paper argues, is more attractive in reducing risk. In contrast to the conventional approach of trading the daily peak and trough, multiple trades are found to be profitable and opportunistic depending upon the weather forecasts. [ABSTRACT FROM AUTHOR]

"Are electric cooking appliances viable clean cooking solutions for mini-grids?" To help answer this question, the Access to Energy Institute (A2EI) set up a pilot project in six different mini-grid locations around Lake Victoria in Tanzania and gave 100 households an electric pressure cooker (EPC) to use in their homes. Each EPC was connected to a smart meter to collect data on how the EPCs were used. The paper presents findings from a study designed around the A2EI pilot project that aims to provide an understanding of cooking practices, the adoption of electric cooking over time, and to assess the potential for electric cooking to substitute traditional cooking fuels. Through collaboration with the Modern Energy Cooking Services (MECS) program, Nexleaf Analytics, and PowerGen, the pilot has generated data on electrical energy consumption from 92 households in six remote areas as well as a comprehensive range of other datasets gathered from 28 households in two of the locations. This paper presents a preliminary analysis of this data. It starts with an analysis of cooking practices in these communities—dishes cooked, utensils used for cooking, and choice of fuels. It goes on to examine fuel stacking behavior, and finally, it examines how people have integrated EPCs into their cooking practices before the highlighting key impacts associated with using EPCs. The answer to the original research question will be useful for different stakeholders such as utility companies, mini-grid operators, electric cooking appliance manufacturers, the clean cooking sector, and international organizations. [ABSTRACT FROM AUTHOR]

The government of Malaysia has recently decided to explore the feasibility of wind energy to generate electricity in the country. Their ambition is to achieve a measureable target in the percentage contribution of electricity generated by renewable energy technology in the national electricity generation mix. As part of this initiative, a study of wind energy policy has been conducted by identifying the optimal feed-in tariff (FiT) rates to support the development of wind energy in the country. The aim of this paper is to calculate the optimal level of tariff that is suitable with local wind conditions. A closed-form equation for optimal feed-in tariff rate of wind energy with consideration of the availability of capital allowance has been developed. The focus is on small- and utility-scale wind turbine installations. As a result, by considering the availability of capital allowance, the optimal FiT rates for small-scale wind turbines in Malaysia are between 0.9245-1.1313 RM/kWh, while utility-scale rates are between 0.7396 and 0.9050 RM/kWh. The level of FiT is changed with the changing value of economic parameters. Kudat, in northern Borneo, has been identified as a prime site for wind energy development in the country; however, more work needs to be conducted, including the development of a regional wind map and measurement of wind data at more new potential sites. [ABSTRACT FROM AUTHOR]

Power-to-gas is a promising option for storing interment renewables, nuclear baseload power, and distributed energy and it is a novel concept for the transition to increased renewable content of current fuels with an ultimate goal of transition to a sustainable low-carbon future energy system that interconnects power, transportation sectors and thermal energy demand all together. The aim of this paper is to introduce different Power-to-gas "pathways", including Power to Hydrogen, Power to Natural Gas End-users, Power to Renewable Content in Petroleum Fuel, Power to Power, Seasonal Energy Storage to Electricity, Power to Zero Emission Transportation, Power to Seasonal Storage for Transportation, Power to Micro grid, Power to Renewable Natural Gas (RNG) to Pipeline ("Methanation"), and Power to Renewable Natural Gas (RNG) to Seasonal Storage. In order to compare the different pathways, the review of key technologies of Power-to-gas systems are studied and the qualitative efficiency and benefits of each pathway is investigated from the technical points of view. Moreover, different Power-to-gas pathways are discussed as an energy policy option that can be implemented to transition towards a lower carbon economy for Ontario's energy systems. [ABSTRACT FROM AUTHOR]

Power systems require a certain amount of flexibility to meet varying demand and to be able to cope with unexpected events, and this requirement is expected to increase with the emergence of variable power generation. In this paper, we focus on gas engine power plant technology and the beneficial influence its flexible operation can have on a power system. The study introduces the concept of a combined-cycle gas engine power plant (CCGE), which comprises a combination of several gas-fired combustion engines and a steam turbine. The operation of CCGE is then comprehensively analyzed in electricity and reserve production in the South African power system and compared with combined-cycle gas turbine (CCGT) technology. Even though CCGE is a form of technology that has already been commercialized, it is rarely considered as a source of flexibility in the academic research. That is the notion providing the motivation for this study. Our core contribution is to show that the flexibility of CCGE can be valuable in power systems. The methodology is based on the unit-level model of the studied system and the solving of a day-ahead unit commitment problem for each day of the simulated 11-year period. The simulation studies reveal how a CCGE is able to offer system flexibility to follow hourly load variations and capacity to provide reserve power effectively. [ABSTRACT FROM AUTHOR]

In this paper, considering real time wind power uncertainties, the strategic behaviors of wind power producers adopting two different bidding modes in day-ahead electricity market is modeled and experimentally compared. These two different bidding modes only provide a wind power output plan and a bidding curve consisting of bidding price and power output, respectively. On the one hand, to significantly improve wind power accommodation, a robust market clearing model is employed for day-ahead market clearing implemented by an independent system operator. On the other hand, since the Least Squares Continuous Actor-Critic algorithm is demonstrated as an effective method in dealing with Markov decision-making problems with continuous state and action sets, we propose the Least Squares Continuous Actor-Critic-based approaches to model and simulate the dynamic bidding interaction processes of many wind power producers adopting two different bidding modes in the day-head electricity market under robust market clearing conditions, respectively. Simulations are implemented on the IEEE 30-bus test system with five strategic wind power producers, which verify the rationality of our proposed approaches. Moreover, the quantitative analysis and comparisons conducted in our simulations put forward some suggestions about leading wind power producers to reasonably bid in market and bidding mode selections. [ABSTRACT FROM AUTHOR]

Accurate modeling and forecasting monthly electricity consumption are the keys to optimizing energy management and planning. This paper examines the seasonal characteristics of electricity consumption in Hong Kong--a subtropical city with 7 million people. Using the data from January 1970 to December 2014, two novel nonlinear seasonal models for electricity consumption in the residential and commercial sectors were obtained. The models show that the city's monthly residential and commercial electricity consumption patterns have different seasonal variations. Specifically, monthly residential electricity consumption (mainly for appliances and cooling in summer) has a quadratic relationship with monthly mean air temperature, while monthly commercial electricity consumption has a linear relationship with monthly mean air temperature. The nonlinear seasonal models were used to predict residential and commercial electricity consumption for the period January 2015--December 2016. The correlations between the predicted and actual values were 0.976 for residential electricity consumption and 0.962 for commercial electricity consumption, respectively. The root mean square percentage errors for the predicted monthly residential and commercial electricity consumption were 7.0% and 6.5%, respectively. The new nonlinear seasonal models can be applied to other subtropical urban areas, and recommendations on the reduction of commercial electricity consumption are given. [ABSTRACT FROM AUTHOR]

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. The k-NN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE) is 42 W/m2 and the root-mean-square error (RMSE) is 242 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

In a carbon-constrained world, natural gas with low emission intensity plays an important role in the energy consumption area. Energy consumers and distribution networks are linked via energy hubs. Meanwhile, reconfiguration that optimizes operational performance while maintaining a radial network topology is a worldwide technique in the electricity distribution system. To improve the overall efficiency of energy infrastructure, the expansion of electricity distribution lines and elements within energy hubs should be co-planned. In this paper, the co-planning process is modeled as a mixed integer quadratic programming problem to handle conflicting objectives simultaneously. We propose a novel model to identify the optimal co-expansion plan in terms of total cost. Operational factors including energy storages and reconfiguration are considered within the systems to serve electricity, cooling and heating loads. Reconfiguration and elements in energy hubs can avoid or defer new elements' installation to minimize the investment cost, maintenance cost, operation cost, and interruption cost in the planning horizon. The proposed co-planning approach is verified on 3 and 12-node electricity and natural gas distribution systems coupled via energy hubs. Numerical results show the ability of our proposed expansion co-planning approach based on energy hub in meeting energy demand. [ABSTRACT FROM AUTHOR]

Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets. [ABSTRACT FROM AUTHOR]

Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD), seasonal adjustment (S), cross validation (C), general regression neural network (GRNN) and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR). The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour's electricity demand in two data sets (New South Wales (NSW) and Victorian State (VIC) in Australia). Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness. [ABSTRACT FROM AUTHOR]

Policymakers in developing and transitional economies require sound models to: (i) understand the drivers of rapidly growing energy consumption and (ii) produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios--as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis)--it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 412 % per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources, consequently GDP is heavily influenced by oil prices; hence, real non-oil GDP is employed as the activity driver in this research (unlike almost all previous aggregate energy demand studies). Moreover, electricity prices are administered rather than market driven. Therefore, different cointegration and error correction techniques are employed to estimate a number of per capita electricity demand models for Azerbaijan, which are used to produce forecast scenarios for up to 2025. The resulting estimated models (in terms of coefficients, etc.) and forecasts of electricity demand for Azerbaijan in 2025 prove to be very similar; with the Business as Usual forecast ranging from about of 19 1/2 to 21 TWh. [ABSTRACT FROM AUTHOR]

Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP) model. In addition, the results are discussed, and the conclusions are drawn. [ABSTRACT FROM AUTHOR]

Cogeneration and trigeneration plants are widely recognized as promising technologies for increasing energy efficiency in buildings. However, their overall potential is scarcely exploited, due to the difficulties in achieving economic viability and the risk of investment related to uncertainties in future energy loads and prices. Several stochastic optimization models have been proposed in the literature to account for uncertainties, but these instruments share in a common reliance on user-defined probability functions for each stochastic parameter. Being such functions hard to predict, in this paper an analysis of the influence of erroneous estimation of the uncertain energy loads and prices on the optimal plant design and operation is proposed. With reference to a hotel building, a number of realistic scenarios is developed, exploring all the most frequent errors occurring in the estimation of energy loads and prices. Then, profit-oriented optimizations are performed for the examined scenarios, by means of a deterministic mixed integer linear programming algorithm. From a comparison between the achieved results, it emerges that: (i) the plant profitability is prevalently influenced by the average "spark-spread" (i.e., ratio between electricity and fuel price) and, secondarily, from the shape of the daily price profiles; (ii) the "optimal sizes" of the main components are scarcely influenced by the daily load profiles, while they are more strictly related with the average "power to heat" and "power to cooling" ratios of the building. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tails. [ABSTRACT FROM AUTHOR]

Among the several typologies of storage technologies, mainly on different physical principles (mechanical, electrical and chemical), hydrogen produced by power to gas (P2G) from renewable energy sources complies with chemical storage principle and is based on the conversion of electrical energy into chemical energy by means of the electrolysis of water which does not produce any toxic or climate-relevant emission. This paper aims to pinpoint the potential uses of renewable hydrogen storage systems in Europe, analysing current and potential locations, regulatory framework, governments' outlooks, economic issues, and available renewable energy amounts. The expert opinion survey, already used in many research articles on different topics including energy, has been selected as an effective method to produce realistic results. The obtained results highlight strategies and actions to optimize the storage of hydrogen produced by renewables to face varying electricity demand and generation-driven fluctuations reducing the negative effects of the increasing share of renewables in the energy mix of European Countries. [ABSTRACT FROM AUTHOR]

The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD) with induced ordered weighted harmonic averaging operator (IOWHA) to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM) forecasting model and multiple regression (MR) model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure. [ABSTRACT FROM AUTHOR]

The increasing growth in power demand and the penetration of renewable distributed generations in competitive electricity market demands large and flexible capacity from the transmission grid to reduce transmission bottlenecks. The bottlenecks cause transmission congestion, reliability problems, restrict competition, and limit the maximum dispatch of low cost generations in the network. The electricity system requires efficient utilization of the current transmission capability to improve the Available Transfer Capability (ATC). To improve the ATC, power flow among the lines can be managed by using Flexible AC Transmission System (FACTS) devices as power flow controllers, which alter the parameters of power lines. It is important to place FACTS devices on suitable lines to vary the reactance for improving Total Transmission Capacity (TTC) of the network and provide flexibility in the power flow. In this paper a transmission network is analyzed based on line parameters variation to improve TTC of the interconnected system. Lines are selected for placing FACTS devices based on real power flow Performance Index (PI) sensitivity factors. TTC is computed using the Repeated Power Flow (RPF) method using the constraints of lines thermal limits, bus voltage limits and generator limits. The reactance of suitable lines, selected on the basis of PI sensitivity factors are changed to divert the power flow to other lines with enough transfer capacity available. The improvement of TTC using line reactance variation is demonstrated with three IEEE test systems with multi-area networks. The results show the variation of the selected lines' reactance in improving TTC for all the test networks with defined contingency cases. [ABSTRACT FROM AUTHOR]

Electricity markets are complex environments with very particular characteristics. Some of the main ones for this complexity are the need for an adequate integration of renewable energy sources and the electricity markets' restructuring process. The growth of simulation tool usage is driven by the need to understand those mechanisms and how the involved players' interactions affect the markets' outcomes. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. Although, they share a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow players from different systems to interact in common market environments. This paper proposes the use of ontologies for semantic interoperability between multi-agent platforms in the scope of electricity markets simulation. The achieved results allow the identification of the added value gained by using the proposed ontologies. They facilitate the integration of independent multi-agent simulators, by providing a way for communications to be understood by heterogeneous agents from different systems. [ABSTRACT FROM AUTHOR]