Retail Electricity Research Articles

A robust benchmarking of direct margin in Italy's energy retail markets

In the 2000s, Italy liberalized its electricity and natural gas markets, and in 2016, it separated retail activities from other activities within these markets. Such differentiation is unique within Europe. Italy is an ideal case for investigating which operational factors may increase profit margins among electricity and gas retailers. The present analysis analyzes 120 retail operators in the Italian electricity and gas markets in 2020, using two models to assess their cost and commercial efficiencies in achieving high direct margin (defined as the difference between revenues and raw materials cost). The findings show a positive effect of size on efficiency.

Specifics of strategic pricing in the current electricity market (energy supply companies)

Subject. This article discusses the principles of functioning of the wholesale and retail electricity markets in Russia. Objectives. The article aims to study the basic principles of pricing, improvement of the applied methods of electricity pricing in the wholesale and retail markets. Methods. For the study, we used the methods of comparative and systems analyses. Results. The article finds that price positions are closely related to the volume of demand, as well as to the effective use of the installed capacity of generating companies. Conclusions. The results of the study can be used in the practical activities of power supply companies.

A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response

The high penetration of electric vehicles (EVs) poses both opportunities and challenges for power systems. EV retailers, playing a critical role in the demand response mechanism, face market risks caused by uncertainties in electricity consumption and prices. To address the operational issues of large-scale EVs and tap the potential of EV retailers, a temporal and spatial domain-based optimization strategy is proposed, which is implemented on bi-level (referring to transmission and distribution grid networks). First, a physical scheduling model of the grid is established, consisting of a novel hybrid demand response mechanism considering the incentives of EV retailers and the retail electricity price of EV users, and a new robust retail electricity pricing strategy handling the uncertainties of EV behavior and the electric market. Then, a bi-level optimization strategy is presented: at the upper level, in the transmission network, based on the robust pricing strategy, a unit commitment model that coordinates the hybrid demand response with other distributed energy resources is designed to optimize load periods of EVs in the time domain; at the lower level, in the distribution network, an optimal power flow model is proposed to spatially dispatch the location of EV loads. The impacts of retail price profile, EV penetration, hybrid demand response mechanism, and EV load location are analyzed in ten tests using the IEEE 33 distribution network. Simulation results indicate that the robust pricing strategy can effectively handle uncertainties, the integration of the hybrid demand response mechanism into scheduling can ensure the benefits of all participants, and the bi-level optimization strategy can accommodate distributed energy resources temporally and spatially.

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%.

Implications of Growing Wind and Solar Penetration in Retail Electricity Markets with Gradual Demand Response

Time-of-use pricing in retail electricity markets implies that wholesale market scarcity becomes easily communicated to end consumers. Yet, it is not well-understood if and how the price formation process in retail electricity markets will help to reward the demand for operational flexibility due to growth in intermittent generation. To contribute to this discussion, this paper develops a partial equilibrium model of the retail electricity market calibrated to Chinese data. The paper finds that tariffs in this market may not be significantly suppressed by growth in near-zero costs renewable sources when controlling for flexibility restrictions on thermal generation assets and when a significant curtailment of variable renewable resources exists in the market. In addition, it shows that the price formation process in retail electricity markets which controls for flexibility restrictions on thermal generation while allowing for consumers to respond slowly to price changes is a feasible strategy to reward the demand for operational flexibility. Finally, the paper reveals that while integrating intermittent generation beyond levels which the available storage capacities can accommodate may result in losses to producers, benefits to consumers may offset these losses, leading to overall welfare gains.

A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading; however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multi-stage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage.

Optimal battery scheduling in solar-plus-storage grid-connected microgrid for profit and cost efficiency: A use case on an Israeli microgrid

This paper presents an optimal energy management algorithm for solar-plus-storage grid-connected microgrid simulated on a real full-scale small town microgrid test-case, taking into account the daily solar energy generation as well as the electricity demand to ensure that the battery is charged and discharged at the optimal times to balance energy supply and demand. The optimization is performed using dynamic programming and finding the minimum-cost-path, to minimize the total cash-flow. The formulation is utilized with two variations such that two business models are considered, optimizing cost efficiency in both aspects of minimizing cost and maximizing profits, according to the scenario. The first model maximizes self-consumption, and the second model is the case of retail electricity providers. The results are compared to real data from a solar-plus-storage grid-connected microgrid located in Israel, that is currently managed by a rule-based scheduling procedure. The results show that by using the optimal control schedule, the revenue increases up to 8% during the spring and autumn when the weather conditions are typically moderate. In summer and winter, where the climate conditions are more extreme and the demand for energy is at its peak, the full revenue potential can be realized by using the suggested optimal control algorithm, offering an increased profit of up to 30%. Unlike prior studies which are restricted to small use-cases, this paper leverages a unique and private large-scale data-set from a small town, enabling a thorough exploration of the optimization’s real-world viability and scalability. Through rigorous simulations, the findings underscore the method’s exceptional potential in effectively integrating PV-plus-storage assets and optimizing energy use in power grids.

Reliability service-based strategies for improving electricity retailer competitiveness using a novel heuristic algorithm

Liberalizing China's electricity sales market has led to fierce competition among electricity retailers. Providing customized electricity sales services is beneficial for electricity retailers to capture market share and gain a competitive edge. Therefore, a reliability service-based strategy is proposed to improve the electricity retailer competitiveness, which attracts industrial users by providing a continuous electricity supply. Specifically, the electricity retailer designs an electricity price package based on supply service reliability and a compensation model to cope with supply interruptions, considering the incremental cost approach and industrial users' different reliability needs. In addition, a user power shift model is constructed to evaluate user satisfaction, including electricity consumption, electricity cost, and reliability satisfaction. Furthermore, a decision-making optimization model for electricity retailers is formulated to maximize competitiveness and solved by the Growth Optimizer (GO). Experiments based on power grid company show that the proposed model can effectively improve the competitiveness of electricity retailers. Comparison with other heuristic algorithms also proves the superiority of the GO algorithm in terms of solution speed and quality.

Electricity trading of multiple home microgrids through V2X based on game theory

Currently, a significant number of householders are contentedly participating in energy markets integrated with renewable energy sources (RES) to control global warming. Further, the high penetration of e-mobility influences the net zero concepts globally. However, uncoordinated scheduling of electric vehicles (EV) battery charging/ discharging parked in residential areas would result in critical issues in the power system in terms of energy availability. Thus, running intelligent charging/ discharging approaches can tackle these obstacles while managing green energy efficiently. This paper proposes a novel smart EV charging/ discharging strategy for multiple home microgrids (H-MG) based on user behavior analytics patterns with a non-cooperative game theory approach named Nikaido–Isoda/Relaxation algorithm (NIRA). In the proposed structure, EVs are active loads besides responsive loads in H-MGs and are involved in demand-side management (DSM) to achieve peak shaving. Furthermore, electricity retailers (ER) are encountered with distinct properties following opposite intentions and could participate in the competitive retail electricity market (REM) in residential areas. Therefore, households can maximize their profit by reducing varied costs, such as EV charging and battery degradation, while minimizing their carbon footprint. In addition, this study works on the distributed decision-making process that could provide optimal power-sharing and the best payoff in an unbiased procedure. After crucial investigations, the simulation outcomes demonstrate the effectiveness of the proposed approach in encouraging various households to participate actively in the REM with other agents to increase their profits. According to acquired results, active residents saved their budget 28%–45% through employing intelligent EV charging/ discharging strategies in the REM with dynamic pricing compared to the fixed tariff.

On the impact of targeted and universal electricity concessions policy on fuel poverty in the NEM's Queensland region

In Australia's National Electricity Market (NEM), households have faced markedly higher electricity prices following the 2022 war in Ukraine and subsequent energy crisis. Forward prices for coal and gas increased sharply, trading at multiples of historic averages. While initially sheltered to wholesale price shocks through regulatory lag, retail electricity tariffs rose by more than 30 % for the 2024 financial year. Impacts of this magnitude warrant adjustment to hardship policy given electricity is an essential service. In the NEM's Queensland region, hardship policy payments for vulnerable households were more than doubled, and a one-off universal payment was made to all households. In this article, we model the number of households that meet a definition of fuel poverty before, and after, the tariff increases and changes to policy. We find the underlying levels of fuel poverty rose from 6.2 % to 11.6 % over the period 2022 to 2023. From a horizontal and vertical policy efficiency perspective, the suite of policy initiatives improve outcomes with the headline result being a material reduction in fuel poverty, falling from 11.6 % to 5.4 % of households.

Stackelberg–Nash game approach for price-based demand response in retail electricity trading

This paper studies the price-based demand response problem in a deregulated retail electricity trading, aiming to coordinate the energy consumption behavior of end-users under dynamic retail prices. The challenge here is that in addition to the hierarchical decision-making process between utility company and end-users considered in existing works, the non-cooperative and competitive interdependence among end-users cannot be ignored. To address this issue, we first construct a novel Stackelberg–Nash game, in which the Stackelberg game is used to capture the hierarchical decision-making process between utility company and end-users, while the Nash game is dedicated to describing the interdependence among end-users. Then the existence and uniqueness of the Stackelberg–Nash equilibrium is provided along with theoretical analysis. On the basis of the analysis of equilibrium, we propose a distributed iterative algorithm with an adaptive step size, which is benchmarked with a fixed step-size algorithm. The comparison results on a real-life residential retail electricity market show that our proposed algorithm has better performance in terms of effectiveness and scalability.

Conceptual principles of forecasting demand on the day-ahead market using changes in hourly bidded demand between previous similar days

On the basis of the Law on the Electricity Market adopted in 2017, the retail electricity market in Ukraine opened on January 1, 2019, and later, on July 1, 2019, the wholesale electricity market was launched. The day-ahead market (DAM) is one of the key segments of the Ukrainian electricity market. In order to implement trading strategy and successfully conduct business, to maximize the economic results in the specified market segment, it is important to understand the market situation and the structure of demand and supply. One of the indicators that must be taken into account when planning sales in the course of one’s activity is the demand for electrical energy. Currently, there are no universal algorithms in Ukraine suitable for short-term (per day) forecasting of the amount of electrical energy that will be traded on DAM. Therefore, to solve such a problem, a specialized forecasting algorithm is proposed. The basis of the developed algorithm is the possibility of considering the formulated problem in a parametric form, where, as indicators, the forecast and real data of the hourly demand on the DAM are used. At the same time, in order to find the forecast hourly demand on the market “a day ahead” – the values of the unknown indicators of the problem – an iterative method of their search is used based on statistical data of the amount of electricity purchases on the DAM, using the principle of multi-iteration analysis of changes in demand for previous similar days. The proposed algorithm is implemented in the MS Excel package, which indicates its versatility and ease of use. The high speed of obtaining a solution to the formulated problem is shown.

A classified retail pricing and production scheduling model for industrial customers

In the electricity retail market, it’s an important operation way for a retailer to provide diversified services and design appropriate retail pricing strategies that stimulate demand side responses in order to increase profit and maintain customer welfare. This paper proposes a novel retail contract design that a retailer can adopt to provide different time-of-use (TOU) prices for the various types of industrial customers based on their adjustabilities and a part of rewards from the demand response (DR) market to those who respond to the urgent electricity requirements. A bi-level optimization model of retail pricing for classified industrial customers is set up based on their adjustable capacities of production process operations. At the upper level, the retailer’s benefit is maximized considering the stochastic risks of load fluctuation and the spot market. This is achieved based on the electricity consumption strategies of classified industrial consumers, which are obtained from the lower level. Decisions are then made concerning purchases from the spot market and retail pricing strategies for industrial customers. Based on the retail prices from the upper level and industrial production processes modeling, the model at the lower level takes the maximum industrial user’s benefit as the objective to obtain the electricity consumption scheme of each type of industrial user. The model optimizes both the time division and price level of retail TOU prices, resulting in detailed electricity consumption strategies for all types of industrial customers. The results of the cases show that the proposed retail pricing can improve adjustable capacities of industrial production process operations, effectively smooth the load curve of the retailer by making full use of the peak-load-staggered among industrial customers, and boost profits of both the retailer and industrial customers. Thus, a mutually beneficial situation between the retailer and industrial customers is realized.

Intelligent optimization algorithm-based electricity pricing strategy for smart building clusters

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strategy.

Drivers of natural gas and renewable energy utilization in the USA: How about household energy efficiency-energy expenditure and retail electricity prices?

Besides the substantial investments in renewable energy development across various economies, natural gas is being widely considered as a transitional fuel to advance global efforts towards achieving carbon neutrality. The study utilizes a dataset spanning the period from 1970 to 2022 and employs recently developed and appropriate econometric methodologies, primarily centered around Fourier functions. Additionally, the analysis includes controls for economic growth and energy expenditure per household to yield findings that are both relevant and actionable for policymakers. Crucially, the bootstrap Fourier quantile causality analysis has revealed several noteworthy findings. Firstly, it indicates that retail electricity prices exert a negative influence on renewable energy consumption, particularly in the upper quantiles, signifying a stronger impact when renewable energy usage is already high. Secondly, household energy efficiency is associated with a reduction in renewable energy consumption, predominantly observed in the first quantile. Thirdly, both energy expenditure per household and economic growth contribute to a notable increase in renewable energy consumption, particularly in the initial three quantiles. Likewise, concerning natural gas consumption, household energy efficiency and retail electricity prices exhibit a dampening effect, albeit in different quantiles–household energy efficiency impacting in the upper quantiles and retail electricity prices affecting the lower quantiles.

Has retail competition reduced residential electricity prices in Texas?

We estimate the impact of introducing retail competition on retail electricity prices paid by residential consumers in Texas's two largest cities, Dallas and Houston. Using the synthetic control method to obtain the counterfactual prices, we find that retail competition raised average prices by $0.0112/kWh ($11.2/MWh) in the transition period from 2001 to 2006 and by $0.0134/kWh ($13.4/MWh) during the period of unfettered competition from 2007 to 2020. However, when the US wholesale natural gas prices are relatively low, actual retail electricity prices in areas opened to retail competition are close to the counterfactual prices that would have prevailed had retail competition not been introduced, as measured by the counterfactual prices estimated using the synthetic control method.

A Bi-Level Decision Framework for Incentive-Based Demand Response in Distribution Systems

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.

Over a century of small hydropower projects in Indonesia: a historical review

BackgroundHydropower is a mature energy technology and one that could play a more important role in providing clean and reliable energy. In small-scale contexts, hydropower is useful for providing electricity access, balancing intermittent resources, and as a potential source of energy storage. This paper provides a comprehensive exploration of the development of the small hydropower (SHP) sector in Indonesia, the world’s fourth most populous country.MethodsTwo research methods were employed: secondary data analysis through a desk review of relevant literature and primary data collection through site visits and expert and stakeholder interviews. Two case studies of micro-hydro applications in community-based rural electrification were analyzed. The paper explores how SHP projects were initiated, lessons learned, and policy recommendations of relevance to further development of distributed small-scale renewable energy in Indonesia.ResultsThe sector commenced during the Dutch Era and now centers on both community-based rural electrification projects and commercial schemes under the independent power producer (IPP) approach. Since the late 1980s, initiatives to implement SHP for rural electrification have flourished through various programs. Key regulatory, economic, and technical barriers include inconsistent and unclear supporting regulations, especially regarding electricity prices; artificially low retail electricity prices; capital and borrowing constraints; advantages provided to fossil fuels; limited technical experience and capabilities of project developers and project sponsors; risks from floods, earthquakes, and landslides; constraints on supporting infrastructure; and limited grid links. The most successful and sustainable SHP projects are ones that provide local economic benefits and for which local communities are empowered with ownership and have responsibility for maintenance.ConclusionsSHP will remain small from a macro perspective but could still play a key role in further improving energy access and equity in remote areas. Key initiatives to facilitate this development could include local-level capacity building and project participation and the adequate pricing of negative externalities from fossil fuel projects. Indonesia’s long experience with SHP carries lessons for other developing countries.

In which US counties are anaerobic dairy digesters being implemented?

There has been limited research on the recent increase in US anaerobic dairy digesters. We use panel data to estimate a double hurdle model that predicts both the probability that a county has a dairy digester and the number of digesters in counties that have them. Dairy farms and retail electricity prices affect dairy digesters, although the parameter estimates are inelastic. Biogas-producing landfills, which reflect policies and infrastructure that enable renewable energy, also affect digesters. Our findings on the conditions that are conducive to dairy digesters will help policymakers consider options for reducing manure methane emissions.

On the welfare effect of retail electricity subsidy in Vietnam

PurposeThis research investigates how subsidy programs in Vietnam's residential electricity market affect consumers' well-being.Design/methodology/approachTwo perspectives are employed: cash transfer and quantity-based subsidy. The effectiveness of cash transfer is measured in three ways: benefit incidence, beneficiary incidence and materiality. The quantity-based subsidy is established under the increasing block rate pricing, with the first two block rates being lower than the marginal cost. To improve the quantity-based subsidy, the research examines the consumer surplus under four proposals.FindingsThe results show that both types of subsidies are ineffective in supporting the poor.Research limitations/implicationsIn order to achieve a more equal distribution among households, the subsidy program should remove all subsidized blocks and reflect the full marginal cost. Changes should be made to the price structure regarding both marginal price and intervals.Practical implicationsTo mitigate the impact of the quantity-based subsidy, the government should improve the cash transfer by reducing extortion and improving targeting efficiency, especially for poor households living in rented houses.Originality/valueThis paper is the first to discuss the welfare effect of the electricity subsidy in Vietnam. First, it comprehensively evaluates the cash transfer subsidy in Vietnam. Second, it suggests a modification in the residential electricity tariff.