Retail Electricity Market Research Articles

Electricity Price and Inflation: Macroeconomic Implications of Real-Time Pricing

Electricity prices can be an important driver of inflation. However, the transmission from wholesale electricity price to inflation depends on the type of pricing mechanism in the retail market. Using the introduction of Real-Time Pricing (RTP) in the Spanish retail electricity market, we analyze the transmission of wholesale electricity price shocks to inflation dynamics and how this transmission is affected by the introduction of RTP. The main findings are that—after RTP was rolled out—electricity price shocks display a swifter transmission to inflation while the maximum impact is around three times higher than in the previous period. This higher transmission is not only generated by a more pronounced reaction of the retail electricity price but is also caused by a greater transmission of these shocks to core inflation. These results imply that introducing RTP impacts inflation dynamics and, therefore, affects how central banks should react to this type of shock. JEL Classification: E31, Q43, Q48

МОДЕЛЬ ОПТИМІЗАЦІЇ ФУНКЦІОНУВАННЯ МІКРОМЕРЕЖІ З СЕС ТА УСТАНОВКОЮ ЗБЕРІГАННЯ ЕНЕРГІЇ

The article provides a description of the microgrid function optimization model for solving the problem of reducing the cost of electricity purchases by the owner of such a microgrid on the retail electricity market. The objective function of minimizing the cost of electricity purchase has been formalized. Approaches to determining the coefficients of penalty functions designed to cut off technically unacceptable modes of the microgrid have been determined. A mathematical model of an energy storage system (ESS) is proposed, in which the functions of calculating the technological costs of electricity are formalized. A system of constraints for the problem of minimization of the cost of electricity purchase is given. Approaches to solving the problem of evaluating the effect of SPP and ESS operation in microgrids using optimization results are determined. Examples of microgrid operation optimization for an enterprise with its own SPP and ESS are presented. The need for additional control of the economic expediency of involving ESS in the regulation of electricity balances at the enterprise was noted. References 17, figures 3, table 1.

Comprehensive model for efficient microgrid operation: Addressing uncertainties and economic considerations

The fabrication of microgrids to harness renewable resources for local load provision has emerged as a promising concept. Efficient energy management and resource utilization within the electricity market have become crucial tasks for microgrid operation. This article presents a comprehensive model that addresses economic and technical considerations, as well as uncertainties associated with load, wind speed, and solar radiation. Specifically, the focus is on optimizing the utilization and energy consumption of controllable loads within a distributed energy system, taking into account consumer preferences and operational constraints. A two-stage planning problem is formulated to minimize microgrid operation costs and consumer payments, while considering load requirements, restrictions, and utility-imposed constraints. To solve this problem, an enhanced algorithm based on the mountain gazelle optimizer with improved local search operators is proposed, significantly enhancing its search capabilities. The retail electricity market is modeled using real-time pricing (RTP) and incentive-based regulation (IBR) tariffs to capture wholesale price fluctuations and discourage simultaneous consumption. Notably, price forecasting becomes essential in scheduling controllable loads, introducing uncertainties that are addressed by a two-point estimate method. Simulation results demonstrate the effectiveness and robustness of the proposed approach, exhibiting low standard deviation and high resilience. The results highlight the potential of the proposed method in achieving cost-effective and reliable operation of microgrids in the face of uncertainties and dynamic market conditions.

A pool-based electricity retail markets integrating renewable energy sources and electric vehicles in the presence of demand response program and conditional value at risk to enhance energy security

In restructured power systems, energy security has become a critical concern, driven by changes in laws and regulations and underscored by a defensive approach implemented by the independent system operator. This alignment highlights a strong interdependence between energy security, energy supply, and the economic aspects of energy management. The paper explores how consumers energy supply in restructured systems can be managed through Demand-Side Management (DSM) and Demand Response (DR) programs, considering economic efficiency and risk in scenarios that include Renewable Energy Sources (RES), suitable storage systems, and electric vehicles (EVs). An energy security index is introduced to evaluate the impact of DR programs on both electrical and thermal energy, considering the economic risks involved. A hybrid pool-based electricity retail market approach is utilized to optimize energy security, supply, and consumption. The significant roles of RES, storage systems, and EVs are highlighted in this context. Economic and environmental optimization is achieved using the Conditional Value at Risk (CVaR) method, facilitating strategic decision-making under varied risk conditions. The energy supply security is further analyzed in the presence of electricity market retailers, exploring different pricing strategies. A Mixed-Integer Programming (MIP) model, implemented in GAMS software with the CONOPT solver, is used to examine pricing methods such as Time-of-Use (TOU) and real-time pricing. Findings reveal that DR programs lead to substantial increases in retailer profit and energy supply security value, with real-time pricing showing a significant 23.5 % increase in energy supply security value and a 24.5 % increase in retailer profit compared to fixed tariff pricing.

Unit Equivalent Distribution Network Pricing for Electricity Retail Market

Unit Equivalent Distribution Network Pricing for Electricity Retail Market

Retail competition among multi-type retail electric providers in social networks

Liberalization of the electricity retail market has intensified the competition among different types of retail electricity providers (REPs), leading REPs to pay more attention to electricity consumers' (ECs') preferences for electricity service quality, such as reliability and flexibility. In addition, social learning also greatly influences consumers' electricity purchase strategies. Therefore, this paper models the price and service quality competition among multi-type REPs in a two-layer electricity retail market network that integrates electricity trading and consumer social learning. Firstly, we construct a two-layer network that transforms the dynamic process of ECs switching REPs and changing neighbors into the reconfiguration of the network structure. The upper layer is a bipartite network to express the dynamic transaction between REPs and ECs, while the lower layer is a random network to represent the learning interaction between ECs. Secondly, considering network externalities, we model a retail competition based on price and ECs' preferences for electricity service quality in the network above and get the equilibrium solutions in different scenarios by simulating the electricity consumption data of 600 London households. The results demonstrate that network externalities hinder the transmission of price signals, and the density of the EC network affects the market game result, while the update speed of the EC network has little effect. This study provides theoretical support for multi-agent decision-making in the electricity retail market.

Incentive Compatible Pricing for Enhancing the Controllability of Price-Based Demand Response

Price-based demand response is widely implemented by the load aggregators (LAs) to exploit the flexibility of massive demand-side resources. However, a key problem in real-life implementation is the controllability of price-based demand response, viz, whether the price can guide the demand to the desired value. To bridge this gap, this paper proposes a close-loop reverse Stackelberg game-based pricing method to better regulate the demand response. In the hierarchical game structure, the non-cooperative competition of heterogeneous demand-side resources in participating demand response constitutes the lower-level, while the determination of pricing policy by LA makes the upper-level. Towards a computationally efficient solution, the mean-field game is adopted to approximate the Nash equilibrium of the large-scale lower level competition, and the indirect formulation of close-loop reverse Stackelberg game is adopted. The accuracy of the approximation is proved to be the (1,2) -hierarchical equilibrium of the original game. Finally, a decentralized solution algorithm with sequential linear programming as outer-loop and mean-field iteration as inner-loop is proposed to price the demand response. Numerical tests on a retail electricity market containing 15,000 heterogeneous demand-side flexible resources validate the effectiveness of the proposed pricing scheme.

From Intent to Inertia: Experimental Evidence from the Retail Electricity Market

From Intent to Inertia: Experimental Evidence from the Retail Electricity Market

Retail Electricity Market Restructuring and Retail Rates

Prior to the 1990s, all U.S. states used a “cost of service (COS)” regulation regime in which investor-owned utilities were allowed to recover prudently incurred costs plus a rate of return on capital expenditures, and retail customers were unable to choose their electricity supplier. From 1996–2000, multiple states passed retail electricity market “restructuring.” This empirical research examines the effect of retail restructuring on electricity prices to final consumers. We find that rates increased in restructured states relative to plausible counterfactuals in the years post-restructuring. But by twelve years after retail restructuring, we no longer observe any difference. We investigate plausible mechanisms, finding evidence that retail prices became more responsive to natural gas prices due to retail restructuring, the timing of which coincided with increases in natural gas prices nationally. We also test for whether restructuring had distributional effects across customer classes and find that in the short run residential customers benefited relative to industrial customers during transition periods, but that this difference does not persist into full implementation.

Operation and evaluation of digitalized retail electricity markets under low-carbon transition: recent advances and challenges

With the growth of electricity consumers purchasing green energy and the development of digital energy trading platforms, the role of digitalized retail electricity markets in the low-carbon transition of electric energy systems is becoming increasingly crucial. In this circumstance, the research work on retail electricity markets needs to be further analyzed and expanded, which would facilitate the efficient decision-making of both market players and policymakers. First, this paper introduces the latest developments in the retail electricity market under low-carbon energy transition and analyzes the limitations of the existing research works. Second, from three aspects of power trading strategy, retail pricing methodology, and market risk management, it provides an overview of the existing operation and mechanism design strategies of the retail electricity market; then, it provides a systematic introduction to the evaluation system and monitoring methodology of electricity markets, which is not sufficient for the current digitalized retail electricity markets. Finally, the issues regarding operation evaluation and platform optimization of the current digitalized retail electricity market are summarized, and the research topics worth further investigations are recommended.

Research on the transaction mechanism of electricity retail market that satisfies the full liberalization of industrial and commercial users

This paper proposes an electricity retail market transaction mechanism that satisfies the full liberalization of industrial and commercial users in the future environment of full liberalization of industrial and commercial users. The method and path of the whole process construction of the electricity retail market trading mechanism are analyzed. Aiming at the situation that a large number of industrial and commercial users all enter the electricity retail market, the electricity retail package system mechanism, diversified development and effective management and control paths have been constructed. Analyzed the countermeasures and mechanism suggestions for market transaction organizers to standardize the operation behavior of electricity sales companies and effectively prevent market risks, put forward relevant suggestions to guide electricity sales companies to diversify the development of retail services, and promote business model innovation, and analyzed the effect of case applications.

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.

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.

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.

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.

Sustainable Development of the European Electricity Sector: Investigating the Impact of Electricity Price, Market Liberalization and Energy Taxation on RES Deployment

Replacing conventional CO2 intensive generation with green electricity from RES constitutes an essential prerequisite of sustainable development. Renewables play a vital role in achieving the UN’s goals for clean low-cost energy production and the reverse of climate change process. Based on a comprehensive dataset including observations for 17 European countries between 2003 and 2020, the present research attempts to unveil the fundamental determinants of RES deployment. A panel FMOLS approach was utilized to provide a detailed analysis of the impact of electricity prices, energy taxes and competition level in both power generation and the retail electricity market on each country’s RES percentage participation in electricity production fuel mix. The final econometric outcomes verified the strong statistical significance of all examined variables for the vast majority of the countries, constituting them crucial aspects of national energy strategies. However, both the actual effects as well as the impact size were found to differ significantly across Europe, signifying the complexity of the EU’s task to develop a unified, autonomous and eco-friendly electricity market based on the principals of a fundamental energy strategy. Contributing to state authorities’ and EU’s colossal effort to deal with the crucial challenges of RES power generation, the paper proposes a series of targeted individual and groupwise policy implications.

A bidirectional loss allocation method for active distributed network based on Virtual Contribution Theory

Loss Allocation is a crucial task in power transmission and distribution services, as it essential for reconciliation of generator-user settlement results. With the availability of distributed generation in recent years, power can flow in both directions along transformers and mainlines in the traditional distribution networks. This makes it difficult to calculate power losses in active distribution networks and correct transactions between generators and users. In this paper, we propose the concept of Virtual Contribution Theory and construct a virtual contribution matrix to illustrating how generators and loads use and occupy distribution networks. By combining Virtual Contribution Theory with Power Flow Tracing, a bidirectional loss allocation method based on the Virtual Contribution Theory is proposed to account for power losses in active distribution network. One key advantage of this method is that it eliminates many shortcomings of traditional loss allocation method, including cumbersome calculation processes, low solution efficiency, and limitations in bidirectional power flow calculations. This method can also fairly allocate power loss between generators and loads in the retail electricity market. We conducted tests on IEEE 33 and IEEE 69 systems to evaluate the feasibility and effectiveness of the proposed method.

Dynamic pricing and strategic retailers in the energy sector: A multi-leader-follower approach

We consider strategic retail pricing in markets, where retail companies buy commodities at fluctuating wholesale prices and resell them to final consumers by applying dynamic retail tariffs. This is of especially large relevance in the context of energy markets where substantial wholesale price fluctuations are observed. Policy makers currently foster the introduction of such dynamic tariff schemes. From a modelling point of view, we propose a multi-leader-follower problem to investigate the implications of strategic retail pricing and we compare the impacts of implementing dynamic tariffs on retailers and final consumers. Our analysis tackles different aspects: first, we formulate the model and provide theoretical results. Second, we develop algorithms, which solve the multi-leader-follower problem and allow us to characterize the resulting market equilibria. Third, we calibrate and solve our framework based on data of the German retail electricity market for the years 2020 and 2021. This allows us to quantitatively assess the impact of introducing real time prices on retailers’ profits and customers’ benefits. As our results show, dynamic real-time pricing on the one hand typically increases market efficiency, which confirms previous results obtained without the explicit consideration of strategic behavior. On the other hand, however, as a novel aspect, dynamic real-time pricing turns out to significantly reduce equilibrium profits in case of strategic firms. This effect is especially large in environments with strongly fluctuating wholesale prices.