Energy Purchase Research Articles

Smart transactive energy based approach for planning and scheduling in multi-looped microgrid distribution network across planning horizon

In this paper, an innovative transactive energy approach is proposed as viable option for coordinated distribution system planning across a certain horizon. The proposed approach is evaluated across a multi-looped (meshed) test system and is implemented with load growth having prosumers participating in the electrical market in transactive energy system aiming at evaluation on techno-economic basis. Apart from prosumer sensitivity analysis, evaluations have been carried across reducing total production cost of energy, reduction in per unit price, active power losses. Whereas improving voltage profile, cost of scheduling and consumer per kWh purchase and sales in comparison with traditional counterpart. The proposed framework includes optimization algorithm aiming at sources scheduling and IEEE 69 system for validation. The algorithm minimizes cost, maximizes energy efficiency, increases renewable energy mix and reduces consumers cost of energy purchase. Reduction of 51.44 % in cost of energy is achieved, whereas loss reduction of 12.6% is achieved. The comparison of IEEE 69-bus base case with the 10 %, 15% and 20% transactive energy applied with simulations to evaluate performance parameters that will directly benefit both prosumers and utility alike in-terms of low bills and further reduction of stress on the grid amid load growth across multiple years.

Research on energy purchase combination strategies for multiple energy commercial buildings considering multistage market uncertainties

With the gradual development of the power markets, various types of uncertainty are gradually increasing. The overlapping effects of various uncertainties have a significant impact on the operational economy of multiple energy commercial buildings (MECB) relying on various energy sources. MECB can purchase electricity from multiple stages of the market, such as spot from medium to long term and the market electricity price risk varies at different stages. Therefore, this paper investigates the energy purchasing strategies of MECB in multistage markets, taking into account various uncertainties. Considering the actual operational characteristics of MECB, this paper proposes a MECB low-carbon economic optimization model and establishes an energy purchase strategy model for MECB under various risk scenarios based on random optimization theory. Research has found that in scenarios with higher risks, MECB will participate more conservatively in the market. By simultaneously participating in the medium to long-term market and spot market, MECB can effectively avoid the cost risks brought about by various uncertainties.

Predictive Modeling of Renewable Energy Purchase Prices Using Deep Learning Based on Polish Power Grid Data for Small Hybrid PV Microinstallations

Predictive Modeling of Renewable Energy Purchase Prices Using Deep Learning Based on Polish Power Grid Data for Small Hybrid PV Microinstallations

Biogas Energy: Unexplored Source of a Renewable Energy in Jordan

Jordan is a non-oil producing country. Its basic energy requirements are obtained from imported oil and natural gas from different sources. Domestic natural gas covers only 4% of the Kingdom’s energy needs. Energy import costs create a financial burden on the national economy. Jordan spends more than 25% of its GDP on the purchase of energy. Considerable efforts have been made and great progress has been achieved in the application of solar, wind, biogas and hydro energy utilization. This paper explores the potential of biogas as renewable energy in developing countries including Jordan, where access to basic clean energy services is essential for sustainable development. A techno–economic feasibility study for electric power generation from municipal solid waste was carried out in cooperation with the UNDP and the UN Global Environmental Facility has approved the award to finance a pilot biogas plant at Amman municipal waste disposal site. The project rated capacity is 1MW and due to the successful operation, the project was expanded to 3.5MW.

Characterization of Production under feed-in tariffs in Portugal Mainland

EDP Serviço Universal (EDP SU), in its core business, must ensure the purchase of energy from producers under special regime and distributed production, with feed-in tariffs, and sell it through spot and future markets. Furthermore, the company must ensure the purchase of energy in those markets for the supply of its customers. The process of buying and selling electricity is quite complex from the legislative and regulatory point of view. In Portugal, over the last two and a half decades, high focus on the promotion of renewable energies has been set, particularly through power plants with feed-in tariffs, which has increased the use of internal energy resources and has stimulated the reduction of the country’s energy dependence. During this process, the development of related legislation and the dynamism of the sector, with the introduction of incentives which prompted the investment and promoted the maturation of new technologies, has been essential. The present work characterizes the producers to whom EDP SU purchases energy, with emphasis on technologies, number of installations, installed power, geographic location and acquired energy, showing the evolution over last years.

Integrated energy system for low-carbon economic operation optimization: Pareto compromise programming and master-slave game

An integrated energy system (IES) can effectively solve the energy crisis, realize multi-energy complementarity, and promote fine-grained energy development. Aiming at the low-carbon economy problem of IESs with combined heat and power (CHP), carbon capture systems (CCSs) and power-to-gas (P2G), the joint operation mode of CHP-CCS-P2G is proposed, and the output characteristics and carbon emissions of CHP units under this mode are analysed. On the supply side, a multi-objective optimization method based on compromise programming is proposed to solve the contradiction between economic and environmental objectives in the IES, and the energy operator balances economic and environmental performance to obtain the optimal configuration scheme and operation strategy. On the load side, a master-slave game energy trading strategy considering integrative demand response (IDR) is adopted to balance the interests of the energy operator and users centrally managed by the load aggregator to ensure the fairness of transactions between the two parties. The results show that the overall revenue of the energy operator decreases by 7.99 % after considering IDR. However, the carbon emissions decreased by 933.82 kg, the carbon trading revenue increased by 45.18 %, and the energy purchase cost of users decreased by 9.34 %.

Evaluation of Greenhouse Gas Emissions and Mitigation Measures at Thammasat University's Lampang Campus in Thailand.

Greenhouse gases (GHGs) are the primary drivers of global climate change. Human activities, particularly those related to energy production, transportation, and industry, have long contributed to the escalating levels of GHGs in the Earth's atmosphere. Recognizing the significance of this issue, universities, including Thammasat University, play a vital role in Greenhouse gas (GHG) emissions research and education, carrying a responsibility to address the matter. This study is aimed aims to assess the greenhouse gas emissions and mitigation measures at Thammasat University (Lampang campus), Thailand. The emissions are categorized into 3 types: (1) direct GHG emissions; (2) energy-related indirect GHG emissions; and (3) other indirect GHG emissions. Activity data from the years 2019 to 2022 was used for the calculations, resulting in GHG emissions of 1051.70, 778.28, 558.64, and 1034.531 tons of carbon dioxide equivalent. Among these emissions, energy-related indirect GHG emissions from electricity purchases represent the majority, accounting for approximately 78.55% of the total emissions. Consequently, implementing mitigation strategies, such as solar panel installations and solid waste reduction (combined scenario), has the potential to reduce GHG emissions by up to 57.78%. Furthermore, the university should actively promote GHG emissions reduction through the enactment of energy-saving policies and the adoption of energy-efficient technologies to reduce reliance on energy purchases.

Charging/Refueling Navigation Strategies for Plug-in Hybrid Hydrogen and Electric Vehicles With Irrationalities and Energy Substitution

Electric vehicles (EVs) are believed to be effective in reducing the use of fossil fuels. However, the increasing penetration of EVs can lead to challenges in managing EV charging/refueling. Typically, when EVs simultaneously gather at the stations, long queueing lengths and waiting times might occur, which might result in EV disutility. Thus, in this paper, novel decision-making strategies considering the irrational behaviors of EVs are formulated to ensure the utility of EVs. Three types of EVs are investigated, i.e., plug-in electric vehicles (PEVs), fuel cell electric vehicles (FCEVs), and plug-in hybrid hydrogen and electric vehicles (PH2EVs). First, three typical types of irrationalities are modeled based on prospect theory and irrational herding theory, including risk attitude irrationality, probability distortion, and irrational herding behaviors. Second, decision-making strategies, including station selection, navigation, and energy purchasing, are modeled based on EV irrationalities. Additionally, when making the station selection decision, the selection ranking index is proposed to balance energy price and waiting time. Third, the energy substitution of PH2EV is investigated to determine the optimal energy-purchasing mixture of electricity and hydrogen. Simulation results show that the average waiting time and charging/refueling time of EV users of the proposed model are reduced compared with the other two cases where EV users are assumed to be rational. In addition, the overall utility of EV users is enhanced.

Comparative analysis of decision making factors of hybrid and emission free heat source users in single family houses

Aim/purpose – Energy is traditionally produced using fossil fuels as raw materials, which impacts the environment negatively. Due to the scarcity of fossil fuel supplies, rising prices of energy carriers, and global trends, consumers are turning to renewable energy sources (RES) for home heating. The aim of this study was to determine whether the choice of house heating system using different sources than fossil fuels is driven by any additional components of single-family house users’ purchasing decisions besides cost. Based on research results, the prospects for further transformation of thermal ener- gy in single-family housing in Poland towards RES were determined. Design/methodology/approach – The research on the inhabitants of single-family houses was conducted in December 2022 in Poland. The research method used was a survey using the CATI and CAWI techniques. 600 respondents filled out the question- naire. The results were statistically analyzed. A non-parametric Kruskal-Wallis test (non- -parametric ANOVA) and a post-hoc test were used. The distributions’ normality and the variances’ homogeneity were measured using Kolmogorov–Smirnov and Brownian– –Forsythe tests. Calculations were performed using Statistica software. Findings – The choice of heating energy source in single-family houses, taking CO2 emis- sions into account, is not dictated by income level. Instead, it is determined by the number of people in the household. More numerous households opt for a zero-emission heat source. Households using emission-based heating energy sources are significantly more driven by the cost of using heating appliances; their purchase price, reliability, and product quality are more important than those using zero-emission methods. The partial use of renewable energy source solutions promotes further investment in emission-free heat sources. Research implications/limitations – The analysis illustrates the state of thermal trans- formation in single-family houses during the energy transition process at the national level. A limitation of the research is that it samples only one EU country struggling with too high CO2 emissions compared to other countries. Originality/value/contribution – A unique approach used in this study is to address the variation in incentives for renewable energy purchasing decisions, considering CO2 emissions. This aspect, although noticeable to some extent, is not directly taken into account by users who do not have the tools to assess their CO2 emissions. However, the proposed research approach showed that the CO2 emission level of a heating system is a factor differentiating some aspects of the decision-making process of system users. In particular, it showed what elements of the decision are essential in households that have not yet taken any action towards energy transformation. Keywords: energy, decision-making process, heat source, hybrid heat sources, emis- sion-free heat sources, consumers’ characteristics. JEL Classification: Q56, G51, O13.

The environmental impact of beef and ultra-processed food consumption in Brazil.

This study evaluated the independent and combined environmental impacts of the consumption of beef and ultra-processed foods in Brazil. Cross-sectional study. Brazil. We used food purchases data from a national household budget survey conducted between July 2017 and July 2018, representing all Brazilian households. Food purchases were converted into energy, carbon footprints and water footprints. Multiple linear regression models were used to assess the association between quintiles of beef and ultra-processed foods in total energy purchases and the environmental footprints, controlling for sociodemographic variables. Both beef and ultra-processed foods had a significant linear association with carbon and water footprints (P < 0·01) in crude and adjusted models. In the crude upper quintile of beef purchases, carbon and water footprints were 47·7 % and 30·8 % higher, respectively, compared to the lower quintile. The upper quintile of ultra-processed food purchases showed carbon and water footprints 14·4 % and 22·8 % higher, respectively, than the lower quintile. The greatest reduction in environmental footprints would occur when both beef and ultra-processed food purchases are decreased, resulting in a 21·1 % reduction in carbon footprint and a 20·0 % reduction in water footprint. Although the environmental footprints associated with beef consumption are higher, dietary patterns with lower consumption of beef and ultra-processed foods combined showed the greatest reduction in carbon and water footprints in Brazil. The high consumption of beef and ultra-processed foods is harmful to human health, as well as to the environment; thus, their reduction is beneficial to both.

Application of game theory in modern electrical power system (a review)

In recent decades, because of the speedy progress of the smart grid and the deepening of the reforms of the energy system, demand-side users can contribute to the collaboration of the energy network, with the main right to public energy procurement and the main right to sell energy. The demand side of the smart grid and the open energy market provides users with more adoptions, and game theory is expected to become an important tool for optimizing multi-stakeholder decision-making and solving many problems in this area. In this regard, this review article first reviews the recent development of game theory application in modern power systems, in addition to discussing in detail the typical gaming behavior of the current energy demand side. Second, it focuses on the application of game theory mainly in three aspects: distributed energy users, high-energy energy users, and medium- and low-energy users. Game theory is used in optimizing the distributed energy coordination, optimizing the energy purchasing strategy, responding to the needs of commercial and residential users, and ensuring network communication. Later, a comprehensive analysis of recent trends in game theory application in power systems is carried out in detail.

Distributed Online Incentive Scheme for Energy Trading in Multi-Microgrid Systems

Microgrids (MGs) are essential components of a smart grid, and they play an important role in assisting smart grid operation. Furthermore, multi-MG systems, which can further improve smart grid operation, raise significant challenges in terms of management and interoperation because of their increased complexity. To address this, we propose a multi-bid online incentive scheme for energy trading in multi-MG systems, in which MGs act as buyers and sellers to trade surplus energy. In the proposed system, buyer MGs can submit multiple bids to different sellers, and the distributed MG system operator matches buyer and seller MGs in a way that maximizes the utility of buyers. Theoretical analysis demonstrates that the proposed method meets the properties of incentive compatibility and individual rationality. Moreover, through detailed performance evaluation, the proposed scheme was capable of achieving better performance in comparison with existing schemes regarding utility, energy purchasing cost, and buyer MG’s satisfaction ratio. Our experimental results also demonstrated that the proposed scheme could shift the system peak-load, improving the robustness of the multi-MG system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this article, we addressed the energy trading issues in multi-microgrid (MG) system, and proposed a distributed incentive mechanism to achieve more flexible and efficient energy allocation. Most existing works on energy trading use centralized methods, which means all electrical users will submit only one bid to the platform, and finally the platform decides to match the trading parties. This will lead to users unable to actively choose trading partners, thus reducing the volume of the whole market. To this end, in this paper, we proposed a distributed incentive mechanism for the multiple microgrid system, in which the buyer microgrids are able to submit multiple bids to different sellers. Moreover, each seller microgrids act as a distributed energy market, and the distributed MG system operator matches the buyers and sellers which aims to maximize the utility of buyers. The proposed scheme is helpful in managing the energy trading among the multi-MG system and can be readily implemented in the real-world large-scale electricity trading market.

Association Between Household Online Grocery Delivery Service Use and Food and Drink Purchase Behavior in England: Cross-Sectional Analysis.

Online grocery delivery services (OGDSs) are a popular way of acquiring food. However, it is unclear whether OGDS use is associated with the healthiness of purchases and whether there are sociodemographic differences in OGDS use. If so, the increased prevalence of OGDS use may have implications for population diet, and differential OGDS use could contribute to diet inequalities. This study aimed to examine whether OGDS use varies by sociodemographic characteristics and is associated with the amount and types of groceries purchased. Item-level take-home food and drink purchase data (n=3,233,920 items) from households in London and the North of England were available from the 2019 UK Kantar fast-moving consumer goods panel (N=1911). Purchases were categorized as being bought online or in-store. We used logistic regression to estimate the likelihood of an above-median frequency of OGDS use by sociodemographic characteristics. We used Poisson regression to estimate the differences in energy and nutrients purchased by households that had above- and below-median OGDS use and the proportion of energy purchased from products high in fat, salt, and sugar (HFSS) online versus in-store among households that used both shopping methods (n=665). In total, 668 (35%) households used OGDSs at least once in 2019. Of the households that used OGDSs, the median use was 5 occasions in 2019. Households were more likely to have above-median use in London versus in the North of England (odds ratio 1.29, 95% CI 1.01-1.65) and if they had a higher annual household income (odds ratio 1.56, 95% CI 1.02-2.38 for ≥£50,000 [US $64,000] vs <£20,000 [$25,600]). Households with above-median OGDS use had a higher weekly mean purchase of energy by 1461 (95% CI 1448-1474) kcal per person compared with households with below-median OGDS use. For households that used a combination of in-store and online shopping, HFSS products made up a lower proportion (-10.1%, 95% CI -12% to -8.1%) of energy purchased online compared to in-store. Differences in grocery purchases between households with above- and below-median OGDS use could have positive or negative consequences. The extra energy purchased among households with above-median OGDS use could lead to overconsumption or food waste, which has negative consequences for population and environmental health. Alternatively, this extra energy may be replacing out-of-home purchasing, which tends to be less healthy, and may be beneficial for the population diet. Households made fewer HFSS purchases when shopping online compared to in-store, which may be due to differences in the shopping environment or experience, such as fewer promotions and advertisements when shopping online or not having to transport and carry purchases home. As higher-income households used OGDS more frequently, the implications of this sociodemographic pattern on dietary inequalities must be explored.

Choosing both/and: Encouraging green energy purchases in community choice aggregation

Community Choice Aggregation (CCA) programs offer green electricity products to satisfy customers’ demand for renewable energy. However, it is not clear how consumers approach combining renewable procurement options, such as installing rooftop solar and subscribing to green electricity. This study estimates the probability that residential CCA customers choose to purchase green energy at a price premium when they have already adopted (1) rooftop solar, (2) non-solar distributed energy resources such as electric vehicles, and (3) a time-varying electric rate. Based on residential customer data from Silicon Valley Clean Energy, a CCA in California, we fit a multi-level probit model that accounts for the potential clustering of green participation rates at the zip code level. Our findings suggest that households with installed solar panels, electric vehicles, time-varying electric rates, and lower energy consumption are more likely to purchase green electricity. Ultimately, this study provides insight into strategies for incentivizing consumers to combine green energy options to achieve broader goals related to climate change and grid resilience.

Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models

Due to increasing environmental concerns and demand for clean energy resources, photovoltaic (PV) systems are becoming more prevalent. Considering that in several instances, customers pay for both energy and power, PV installations not only must reduce the customers’ energy purchases but also lower their peak demand for maximum financial benefits. However, in many cases, the peak demand does not coincide with the peak of photovoltaic generation. To address this issue, excess energy generated during low-demand periods can be stored in a battery, which can then be used to meet peak demand. Determining the optimal size of photovoltaic and battery components while ensuring system performance and financial benefits is significantly challenging. This study proposes a novel statistical methodology for optimizing PV-battery system size. In the proposed method, the PV-battery system must meet peak demand thresholds with a specific probability. Further, cost and benefit functions are used for financial evaluation. Finally, Monte Carlo simulations, developed using time series clustering and a Bayesian model are utilized to assess system performance and financial feasibility.

A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy

Under the policy of renewable portfolio standard (RPS), many microgrid (MG) users with high electricity consumption are installed with renewable energy (RE) generation devices to complete their RE consumption responsibility and reduce carbon emissions. In view of the conflict between the goals of microgrid operators (MGO) and MG users, it is a challenge to achieve economic optimization for both MGO and MG users at the same time when they participate in system dispatch and complete RE consumption responsibility. Therefore, this paper proposes a bi-level noncooperative dispatch strategy that takes into account user satisfaction and RE consumption willingness. In this bi-level model, the upper level is led by an MGO who decides electricity retail prices, while the lower level is followed by MG users who participate in demand response (DR) and change electricity purchase strategies. First, a novel comprehensive evaluation model is formulated to evaluate users' willingness to consume green electricity. Based on this, users' consumption behavior of green and thermal electricity is modeled independently not only in electricity trading but also in power dispatching. Second, optimization objectives are established with the goal of maximizing MG users' welfare, including user satisfaction improvement and energy purchase cost reduction, as well as maximizing the revenue of the MGO. Then, a Stackelberg game model is constructed, and a differential evolution algorithm nested CPLEX is used to solve the Stackelberg game problem of balancing the interests of both parties. Finally, our simulation analysis shows that under the dispatch strategy in this paper, load peak-valley difference is reduced. In addition, when the green electricity generated within the system is sufficient, based on a high level of users' willingness to consume green electricity, the total consumption of green electricity is increased by 29.5% and the overall welfare of users is increased by 0.75%.

Low-carbon optimal operation of the integrated energy system considering integrated demand response

In order to further reduce the carbon emission level of the integrated energy system and improve the system operation economy, a low-carbon economic dispatch strategy for the integrated energy system considering comprehensive demand response is proposed. First, based on the composition and operating characteristics of the integrated energy system, two demand response models, price-based and substitution-based, are established. Secondly, based on the ladder-type carbon trading mechanism and the green certificate trading mechanism, a green certificate-carbon trading mechanism model is designed to construct a ladder-type carbon trading mechanism model. A comprehensive energy system low-carbon economic dispatch model with the lowest system operating cost is composed of carbon transaction costs, energy purchase costs, operation and maintenance costs, green certificate trading income and wind and light abandonment costs. Finally, through simulation experiments, this paper is verified the effectiveness of the proposed model is analyzed, and the impact of demand response sensitivity and ladder-type carbon trading mechanism parameters on system dispatch results is analyzed.

Multiyear stochastic wind generation investment planning with demand response in distribution system using improved water evaporation optimization

ABSTRACT This article proposes a multiyear distributed generation (DG) investment planning model with the coordination of demand response for making the decision of investment under uncertainty. The aim is to reduce the net present value related to the energy purchasing cost from the grid, investment cost including operation, and maintenance cost, emission penalty cost and energy losses cost. In this article, two investment planning models are developed with a total planning horizon of 20 years: (i) static investment planning model and (ii) dynamic investment planning model. In the static investment, DG is installed in first year, whereas for dynamic investment model, time period of DG installation is considered flexible. The impact of real-time demand response is also analyzed for both cases. The optimal distribution network investment planning problem, which includes multiple DG and demand response, is a complicated combinatorial optimization problem that traditional optimization methods are unable to tackle efficiently. Therefore, an improved water evaporation optimization algorithm is proposed and compared with the genetic algorithm, basic water evaporation algorithm, and particle swarm optimizationin this article. Both DG investment models are applied to the standard 33-bus radial distribution network.

Techno-economic viability and future price projections of photovoltaic-powered green hydrogen production in strategic regions of Turkey

This study assesses the economic feasibility of utilizing photovoltaic power for producing green hydrogen in Mersin, Çeşme, and Bandırma. We considered the factors for site selection, such as regional hydrogen demand, solar energy potential, water availability, export opportunities, and existing infrastructure. The research focuses on generating electricity, hydrogen, and oxygen using a 5 MW photovoltaic system. The research finds that the capacity factor for Çeşme, Mersin, and Bandırma is 18.9%, 17.7%, and 15.4% respectively, determining the optimal electrolyzer size. A comprehensive cost analysis is conducted. We employed two methodologies to evaluate the levelized cost of hydrogen. The first approach integrates the levelized cost of electricity for the photovoltaic system, while the second approach considers the annualized capital and operational expenditure for all system components. The analysis highlights the impact of electrolyzer efficiency and timeframe on hydrogen production costs. Estimated costs for Bandırma in 2023 are US$6.8 per kilogram at 70% electrolyzer efficiency, projected to decrease by 2050. With 80% electrolyzer efficiency, costs in 2023 would be US$5.87 per kilogram, with further reductions projected for 2050. We have observed similar trends for Çeşme and Mersin. In conclusion, this study provides a comprehensive cost estimation, taking into account varying discount rates, energy purchase agreement prices, CAPEX and OPEX values, revenues, and site selection, while considering electrolyzer efficiency, to enhance the economic feasibility of photovoltaic-electrolyzer systems.

Low-Carbon Economic Operation Optimization of Park-Level Integrated Energy Systems with Flexible Loads and P2G under the Carbon Trading Mechanism

Against the background of the “30 × 60” target, low-carbon policies and technologies have become the new starting point and destination of energy conservation and emission reduction in energy systems. Power-to-Gas (P2G), as a new energy conversion mode, provides a new way of consuming energy and reducing carbon emissions. An optimal dispatching model of a park-level integrated energy system considering flexible load and power-to-gas (P2G) participation in the carbon trading market is proposed. Firstly, a comprehensive demand response model for electricity and heat is established according to the system’s flexible load characteristics, adding power-to gas equipment and a combined heat and power (CHP) unit to the system. Secondly, against the background of the carbon trading mechanism and considering the incentive effect of P2G on the carbon trading mechanism, a systematic comprehensive carbon trading cost model is established. Finally, the operation objectives of the low-carbon economy, with minimum energy purchase costs, carbon emission costs, and operating costs, are constructed using a CPLEX commercial solver. The results verify the importance of considering the operational cost of P2G and the feasibility of considering both operating economy and wind power accommodation ability together in integrated energy systems.