Electricity Purchase Research Articles

Development prospects for the common electric power market of the Eurasian Economic Union

Subject. The article considers the process of integration of member States of the Eurasian Economic Union in the field of electric power. Objectives. The focus is on the analysis of the organizational and economic mechanism of the functioning of the common electric power market of the Eurasian Economic Union, taking into account the adopted regulatory legal acts. Methods. The study draws on general scientific research methods. Results. The paper determines foundations of the functioning, condition and development prospects for the common electric power market of the Eurasian Economic Union. It establishes that the possibility for participants from different countries to obtain access to the purchase (sale) of electricity within the framework of the Eurasian Economic Union contributes to increased competition, and the variety of methods of electricity trade enables to ensure transparency of pricing in interstate supplies. Conclusions. The functioning of the common electricity market of the Eurasian Economic Union, which complements the domestic markets of Russia, Belarus, Armenia, Kazakhstan, and Kyrgyzstan, will be regulated by supranational rules for electricity trade, taking into account the specifics of the economic development of each country.

Optimal wind power capacity decision consider commitment contracts under uncertain power supply and electricity demand in China

China has been introducing reforms in the electricity market. Since 2016, electricity retailers are allowed to operate in the market once monopolized by traditional power companies. This regulatory environment demands new types of purchase contracts to remunerate the energy agents and deliver better prices for consumers fairly and sustainably. This paper proposes two new electricity purchase contracts with risk sharing mechanism and studies on wind power supplier's optimal capacity investment problem under supply and demand uncertainty. Firstly, a basic model for fixed demand contract is developed, and the optimal capacity is obtained. Secondly, the fixed commitment order contract and the forecast order contract with risk sharing mechanism are proposed to manage the fluctuation of wind power supply randomness. With backward induction, the optimal investment capacity of wind power supplier is solved, and then the fixed order quantity or forecast demand quantity of the electricity retailer can be obtained. Our results show that the fixed order quantity may have a positive influence on the wind power supplier's capacity investment decision. While, the forecast order quantity may have a negative influence on the wind power supplier's capacity investment decision. In addition, wind density has a strong influence on wind power capacity investment, and it has a positive impact on wind power supplier's profit, but it does not always have a positive impact on electricity retailer's profit. At last, based on numerical analysis, we examine how the wind density distribution coefficient change affect members' profit in the electricity supply chain. We also find that even with wind curtailment punishment, the forecast order contract can make a Pareto improvement on the whole electricity supply chain.

Guarantees of electricity origin as a financial mechanism to increase investment attractiveness in the energy sector

In modern environment, ensuring the development of the renewable energy sector is significant for achieving the country’s sustainable development goals. The introduction of a mechanism for guaranteeing the origin of electricity is one of the most effective ways to develop renewable energy, and therefore its study is relevant. The purpose of this study was to analyse the approaches to the functioning of the market of guarantees of electricity origin in the EU countries and compare them with the situation in Ukraine. The principal methods of the study were forecasting (prospects for the implementation of energy origin guarantees in Ukraine) and comparison (approaches to the formation of the mechanism of energy origin guarantees in Ukraine and the EU). The study noted the overall role of renewable energy for the development of any country and Ukraine specifically. All the main positive components that arise in countries in the development of this area were described in detail. The instrument of guarantees of energy origin stays effective for achieving the goals of renewable energy development in Ukraine. Considering the specific features of the rules for selling renewable energy guarantees, the study concluded that they are useful both for stimulating the development of the sector and for disseminating information about the need to improve the state of the renewable energy sector in Ukraine and globally. The study assessed what steps has Ukraine taken to adopt and implement this mechanism. Information on EU countries was also examined. The gradual increase in demand for such instruments every year since 2019 indicates not only the popularity of this practice, but also its need among all market players. It was shown that, after the start of Russia’s full-scale invasion of Ukraine, the foreign trade balance turned negative, i.e., the volume of electricity purchases exceeded the volume of sales. The findings of this study can be used to formulate a long-term strategy for the development of a mechanism for guaranteeing the origin of electricity in Ukraine by public authorities

Surgical Procedures for a Greener Future: An Approach to Assess the Environmental Impact

The healthcare sector is responsible for around 4.4% of worldwide greenhouse gas emissions, according to estimates. These emissions comprise both direct and indirect sources, with direct emissions encompassing aspects like patient transport, fuel consumption, and anaesthetic gases. Indirect emissions are associated with energy usage, including electricity purchases, as well as the products employed in healthcare, such as drugs and medical supplies. Operating rooms (OR) are responsible for the greatest rate of resource consumption and overall hospital waste, varying from 20% to 33%. Hence, it is essential to comprehend the environmental impact of surgical procedures to obtain insight into the total emissions associated with the healthcare sector. Moreover, the lack of uniformity in data collection and the discrepancy of the data used by researchers makes it challenging, if not ineffective, to conduct a rigorous scientific comparison among the currently available studies on the environmental impacts of surgical procedures. This study aims to provide a practical and standardised framework that can be utilised to evaluate, simply and consistently, the environmental impacts of surgical procedures. The adoption of a uniform methodology guarantees the inclusion of important variables and factors, preventing any oversights in the evaluation of the entire process. This allows for emphasis on the most sustainable techniques and permits the identification of controllable factors that can be modified within a short timeframe and are under the direct control of healthcare professionals. These factors include the selection of disposable or reusable instruments, the use of different anaesthetic gases, and the installation of different equipment.

Optimal design of hydrogen production processing coupling alkaline and proton exchange membrane electrolyzers

This study considered complementation of the different electrolyzers and designed a new hydrogen production system coupling alkaline (ALK) electrolyzers and the proton exchange membrane (PEM) electrolyzers. A collaborative optimization method is proposed to design both the capacities and the annual energy scheduling of the system. Due to the different operation characteristics and parameters of the two electrolyzers, the operation states of them are described separately in the model. Besides, several practical factors are also considered, including electrolyzer grouping, hydrogen production loss, adjustment response efficiency and switch-on times. According to the calculation results, the annual income of the coupling system is 6.0 % and 28.9 % higher than that of the ALK only and PEM only system. The installed load rates of PEM electrolyzers to the coupling systems are between 10 and 20 %. Furthermore, the influences of multiple factors on the coupling system are also analyzed. After analysis and comparison, hydrogen production and system economy rise with increased wind power installed proportion is rising. Besides, the increase of the allowed proportions of electricity purchase can enhance the stability of system power supply, and improve the hydrogen production and economy.

Study on integrated energy microgrid energy purchase strategy with demand-side response in market environment

Integrated energy microgrids (IEM) have emerged as an effective way to improve energy efficiency and promote distributed energy utilization. IEM systems acquire electricity and gas from external markets and supply electricity/heat/cold to users. In this paper, we study the optimal energy purchase strategy for IEM, considering the impact of demand response incentives. Firstly, considering the uncertainties, we construct an IEM medium- and long-term market multi-energy purchase model based on conditional value-at-risk, optimizing the portfolio of electricity and gas purchases, as well as their proportion in total energy amount. Subsequently, based on medium- and long-term daily energy supply curves and day-ahead load forecast results, a spot market energy purchase model is established to optimize the spot purchase of electricity and gas, maintaining the supply-demand balance while minimizing operating costs. Furthermore, we design demand response incentives and develop a master-slave game model between IEM and users to guide the formulation of the energy purchase strategy by incorporating corrected load data as feedback. The energy purchase strategies are resolved by the GUROBI solver, while the optimization of demand response incentives is carried out through the PSO algorithm, all based on the MATLAB platform. The adaptability of the proposed model and strategy is verified.

Levelized cost of electricity in renewable energy communities: Uncertainty propagation analysis

Renewable Energy Communities (RECs) are being deployed all around the World as a technically feasible solution to decreasing users' dependence on fossil fuels. Several demonstration facilities have shown their potential to provide final consumers with clean energy and all associated environmental benefits. However, the economic evaluation of these systems as a whole set is more complex than evaluating generation technologies individually, which can be considered a barrier, and it may be more complicated to calculate its uncertainty with precision. This paper deals with this challenge and adapts a model for the evaluation of the global Levelized Cost of Electricity (LCOE) of a polygeneration microgrid to the characteristics of a typical REC, allowing the assessment of the distribution of the LCOE depending on the uncertainty of the input parameters. Thanks to its simple analytical formulation, the proposed model, that can be used for any combination of technologies (both renewable and conventional), provides relevant information on uncertainty propagation in a symbolic way that avoids the need to run numerical simulations or make assumptions on the distribution of the random input parameters. A case study has been presented, considering a typical small electrical REC with photovoltaic plants and micro wind turbines. Although the model can be defined to any market, as a representative example, it has been evaluated according to the current Spanish and Italian regulations, which are analyzed in depth with reference to the scientific literature. Results show that uncertainties in parameter estimates give rise to a very large scatter in the LCOE, pointing out a set of quantities whose role is crucial for a reliable estimate, among which electricity purchase and selling prices, yearly power load, and self-consumption / virtually-shared energy rates stand out.

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

Effects of time-of-use pricing for residential customers and wholesale market consequences in South Korea

Using a large-scale pilot test on time-of-use (TOU) pricing for residential customers, this study analyzes the effects of TOU pricing on the load patterns of residential customers and on producer surplus in South Korea. We estimate the difference in the electricity demand functions of residential customers across peak, intermediate, and off-peak periods before and after TOU pricing. In addition, we assess the supply curve in the wholesale electricity market and calculate the changes in electricity purchase costs by integrating the supply curve over the range of consumption changes across the periods. This study simulates the effects of TOU pricing on producer surplus that would be obtained if all residential customers in South Korea switch from increasing block pricing to TOU pricing. Our results show that TOU pricing can be an effective measure for load transfer and simultaneously increase producer surplus. We find that although the revenue of electric utility generally decreases, the cost savings are greater and thus producer surplus increases. The results can be a valuable reference for the South Korean government regarding the nationwide expansion of TOU for residential electricity in the future.

Big Data technologies in the process of forecasting electricity generation from solar photovoltaic power plants

This research aimed to develop methods for using Big Data technologies to forecast electricity generation from solar photovoltaic power plants, which is crucial for optimising energy production and increasing the efficiency of solar resource utilisation. The study employed a method of analysing the economic feasibility of using energy storage systems and a comparative analysis of electricity buying and selling prices on the market. An experiment involving software tools and algorithms for processing, analysing, and modelling large volumes of data was also conducted. As a result of the research, methodologies were developed that encompass data collection and analysis, information visualisation, selection and training of forecasting models based on available data, as well as monitoring and testing their effectiveness. Graphical diagrams were constructed to illustrate the stages of data processing and analysis, the process of forecasting electricity generation for different time periods, and the process of training a model based on data, monitoring, and testing the model. Additionally, a graph was created to show the typicality and range of values, and a graph to display the change in electricity prices throughout the day. Furthermore, technological tools for using Big Data were described, the cost of electricity was calculated, and the economic attractiveness of using energy storage systems was assessed. As a result of the research, a potential profit indicator from price arbitrage was established, as well as economic parameters for the feasibility of using energy storage management based on an analysis of differences in electricity purchase and sale prices. The results obtained can be useful for energy companies and organisations involved in the production of electricity from solar photovoltaic power plants, allowing them to optimise energy production and increase the efficiency of solar resource utilisation

Construction and Application of the Double Game Model for Direct Purchase of Electricity by Large Consumers under Consideration of Risk Factors

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers’ contract bidding and large consumers’ electricity purchase strategy in the medium- and long-term contract transactions.

A novel stochastic multistage dispatching model of hybrid battery-electric vehicle-supercapacitor storage system to minimize three-phase unbalance

The unbalanced load distribution, the single-phase connection of renewable energy, and the uncoordinated charging of electric vehicles (EVs) will bring a severe issue corresponding to the three-phase unbalance in modern distribution networks. To deal with this issue, a novel multistage optimal dispatching model with the hybrid energy storage system (HESS), consisting of the battery energy storage system (BESS), EV, and supercapacitor (SC), is proposed in this paper. The first stage is conducted on the day-ahead stage, driven by minimizing the total operation cost and relieving power unbalance, the unbalanced penalty cost is introduced into the optimal dispatching model with the electricity purchasing cost and the degradation cost of HESS. Also, the chance-constraint stochastic programming (SP) model with the coordinated operation of HESS is accommodated to handle the diverse uncertainties of renewable energy, load demand, and EV users’ behaviors. In the intra-day operation stage, a rolling optimization-based dispatch model is formulated to re-optimize the operation of the SC for the rapid response of mitigating the real-time three-phase unbalance caused by renewable energy. The numerical experiments are executed on the IEEE 34-bus three-phase test system. Compared with the cases without considering SC and the degradation cost of HESS, the power unbalance in the proposed model is reduced by 3.27% and 3.62% per day at the real-time stage, respectively, while total operation cost is reduced by 46.77 US$ and 350.62 US$ per day, respectively. The results validated that the proposed model is efficient in reducing the three-phase unbalance under multi-time scales, improving the economic operation of the distribution network.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study, a novel residential virtual power plant (RVPP) scheduling method that leverages a gate recurrent unit (GRU)-integrated deep reinforcement learning (DRL) algorithm is proposed. In the proposed scheme, the GRU- integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets, lowering the electricity purchase costs and consumption risks for end-users. The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process (CMDP) into an unconstrained optimization problem, which guarantees that the constraints are strictly satisfied without determining the penalty coefficients. Furthermore, to enhance the scalability of the constrained soft actor-critic (CSAC)-based RVPP scheduling approach, a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources (RDER). Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs, balancing the supply and demand of the power grid, and ensuring customer comfort.

The selection of an electric vehicle for the existing photovoltaic system - case study in Polish climatic conditions

The article presents an algorithm for selecting an electric vehicle for your photovoltaic system. Generating electricity by distributed photovoltaic systems is a very visible trend across Europe. Individual and institutional owners are looking for opportunities to save large amounts of money spent on the purchase of electricity. Many times they have built a photovoltaic system larger than their current energy needs. The author suggests a solution to increase the energy produced for own needs instead of giving it to the power grid at unfavorable prices. Such a solution is the purchase of an electric vehicle that will be charged with surplus energy. Research on the selection of a vehicle for a photovoltaic system should start with a precise profile of the current energy consumption from the power grid, energy production from the photovoltaic system and transferring its excess to the power grid. The next step is to characterize the potential electric vehicle. Such characteristics include determination of the energy capacity of the traction battery and the methods of its charging. The final stage is the analytical confirmation of the choice made. For this purpose, the Metalog family of distributions was used to determine the probability of generating the appropriate hourly amount of energy needed to charge an electric vehicle.

Assessment of renewable power generation applied in homestay hotels: Energy and cost-benefit considering dynamic occupancy rates and reservation prices

Increasing the revenue from hotel operations and decreasing the stress on utility grids from hotel energy consumption are two priorities for the modern tourism industry. Dynamic occupancy rate and hotel reservation prices influence their operations revenue, while occupancy rate also impacts real hotel energy consumption. Renewable power generation as an effective measure can benefit hotels not only by decreasing electricity purchases but also increasing some extra income from selling electricity generated from renewable sources. This study investigated the effects of renewable power generation on energy and the cost-benefit performance of hotel operation prices under different occupancy rates and reservation prices. A two-stage energy and cost-benefit performance assessment and a novel comprehensive cost-benefit assessment model were developed to examine these research issues. The impact of the renewable power generation system (i.e., a photovoltaic-battery system installed on the building rooftop) was considered on the hotel energy and cost-benefit performance. It was found that the novel cost-benefit assessment model accurately quantified the overall hotel profit by calculating its energy consumption cost with the PV-battery system under different occupancy rates and reservation prices. Three typical homestay hotels located in Sanya were tested and assessed. The results showed that PV-battery systems effectively decreased the amount of electricity purchased by hotels. Besides, the renewable energy penetration was above 57.98% in various cases. The overall hotel profit increased by a maximum of 31.4% since renewable power generation was used under different occupancy rates and hotel reservation prices.

Predictive power fluctuation mitigation in grid-connected PV systems with rapid response to EV charging stations

Currently, renewable energies and electric vehicle charging stations are essential for energy sustainability. However, the variable generation from renewable sources, such as photovoltaic systems, can lead to power peaks that impact the stability of the grid. This challenge is exacerbated by the increasing demand in fast-charging stations. Addressing these demand peaks is crucial to ensure the stability of the electrical grid. This paper introduces the predictive-flex smoother, an innovative method designed to mitigate power fluctuations in grid-connected photovoltaic systems while optimizing energy management in electric vehicle charging stations. The predictive-flex smoother method incorporates a hybrid energy storage system comprising supercapacitors and vanadium redox flow batteries to respond rapidly to electric vehicle charging stations demands, enhance grid electricity purchase optimization, and improve energy quality delivery. The proposed method integrates two control strategies: photovoltaic fluctuation reduction strategy and peak demand reduction strategy for electric vehicle charging stations. By leveraging prediction algorithms and machine learning techniques, the predictive-flex smoother method achieves precise power fluctuation forecasts, allowing efficient utilization of supercapacitors and vanadium redox flow batteries to smooth photovoltaic power fluctuations and reduce electrical vehicles peak demand. Comprehensive experimental investigations and simulations validate the method's performance under various operational conditions. The results demonstrate the effectiveness of the predictive-flex smoother method, significantly improving the quality of power delivered to the grid while reducing costs. The experimental platform, validates the real-time response of the proposed method, with response times under 500 ms. The experimental results further confirm the efficiency of the method in power smoothing and charging strategies with varying electrical vehicles models and connection coefficients.

Finding Attractive Electric-Vehicle-Charging Locations with Photovoltaic System Integration

The rise of electric mobility poses a challenge and an opportunity for the housing industry to provide charging infrastructure. The housing industry can take advantage of its large roof areas to install photovoltaic (PV) systems and use the electricity generated to charge electric vehicles. This study explores how the charging demand can be allocated to specific locations based on socio-economic parameters and determines whether PV integration is economically viable for EV charging. Two models are used, one with extensive spatial and traffic data to determine the charging demand for over 300 locations, and another with a time-series-based approach for four specific locations to analyze the seasonal dependencies. The results indicate that PV integration is economically advantageous when electricity purchase prices exceed 0.15 EUR/kWh. Higher electricity prices can lead to significant additional profits through PV integration. Slow charging and charging during the day are beneficial, as they increase self-consumption, making PV systems economically viable. However, fast-charging infrastructure should be combined with PV storage systems for effective PV integration.

Impact of the feed-in-tariff exemption on energy consumption in Japanese industrial plants

To promote renewable energy deployment, Japan introduced a feed-in tariff policy in 2012, financed through a surcharge on electricity prices for consumers. The Japanese government also offered a discount system for electricity-intensive industrial plants, exempting them from paying full surcharges. Using monthly plant-level data from 2005 to 2018, this study evaluated the exemption system’s impact on electricity and fossil fuel consumption for plants in the iron and steel, chemical products, and pulp and paper sectors. Our results show that the exempted iron and steel plants decreased electricity purchase and consumption 18.62% and 17.88% after 2017, respectively. This decrease is attributed to the introduction of an electricity efficiency after 2017. It is worth noting, however, that the revision of the scheme did not affect the chemical, pulp and paper sectors. In this sense, a stronger efficiency requirement might be warranted to ensure the decrease in electricity consumption for all sectors. Nevertheless, we estimate that roughly 7 million tCO2 have been avoided thanks to the decrease in electricity consumption in the iron and steel sector.

Risk-based dynamic pricing by metaheuristic optimization approach for electric vehicle charging infrastructure powered by grid integrated microgrid system

This paper presents a risk-based dynamic pricing strategy for the microgrid based electric vehicle charging station. The microgrid is composed of renewable resources such as solar energy, wind energy and fuel cells. The proposed microgrid-based charging station operates off-grid and on-grid depending on the charging demand and trades the electricity with the main grid when needed. In this work, to increase the reliability and to decrease the detrimental impact of electric vehicle load on the distribution network electric vehicles act as a source of flexibility through grid to vehicle and vehicle to grid provisions. The uncertainty of solar and wind sources is a key issue that is efficiently addressed in this work along with one of the electric vehicles load uncertain factors such as state of charge variation using a probabilistic method. Two types of competent time-based dynamic pricing strategies are employed in this work to enhance the collective profit and to curtail the peak-to-valley ratio of charging station and electricity purchase cost of the electric vehicle owner. To deal with the uncertainty in the electricity market and its associated risk, the average value-at-risk is applied. The proposed multi-objective problem is tested on an IEEE 33 bus-modified system using MATLAB software. A metaheuristic fire hawk optimization technique is employed to implement dynamic pricing on charging station for two cases. To observe the efficiency of the applied algorithm, the findings of this work are compared with the other techniques described in the literature.

Optimal distributed generators allocation with various load models under load growth using a meta-heuristic technique

Distribution network planning and operation are facing several problems, including asset congestion, voltage fluctuations, and system instability. The adequate planning and modeling of distributed generators and capacitor banks must quantify these problems. This article presents the optimal allocation of distributed generators in parallel with capacitor banks in distribution networks with single and multi-objectives using the Gazelle Optimization Algorithm (GOA) and Mountain Gazelle Optimization Algorithm (MGOA). The single objective framework includes technical objectives like minimization of active power losses. The multi-objective framework includes technical and non-technical objectives like simultaneously minimization of active power losses, voltage stability, and voltage deviation, and minimization of polluting greenhouse gases and total electricity purchase cost. Furthermore, these planning problems are investigated by three case studies on different nonlinear voltage-dependent models at two different loading conditions from future planning perspectives. The effectiveness and feasibility of the MGOA are evaluated on the IEEE standard 33 bus system. As a result, the MGOA demonstrates a remarkable reduction in technical and non-technical objectives in all types of distributed generator placement. Moreover, a comparative analysis of other existing research works validated the efficiency and feasibility of established algorithms at each use case with different load models by improving all the objective functions of network planning. In single-objective and multi-objective frameworks, the active power losses reduce to 94.42% and 93.57% in the voltage-independent model, respectively. Meanwhile, the non-technical objectives are also significantly improved for each load model, further validating the efficiency of the proposed algorithms.