Retail Electricity Research Articles

Optimal retail contracts with contractible uncertainty

Around the world policymakers and regulators are struggling with the question of how to design retail electricity tariffs in the face of increasing penetration of local generation (e.g., solar PV), smart appliances, local storage, and electric vehicles. There is a widespread recognition that retail tariffs should vary dynamically across time and space, reflecting the changing conditions (congestion and losses) on the underlying networks. But, at the same time, there is recognition that such tariffs potentially expose retail customers to substantial risk. Risk averse retail customers desire protection against price spikes and volatile wholesale spot prices. This paper seeks to derive the optimal retail contract in the special case in which the uncertainty in the market is contractible (in the sense defined here). We show that the optimal retail contract exposes the prosumer to the wholesale spot price at the margin, but also perfectly insulates the customer from risk, achieving the first-best outcome. We show how the hedge component of this retail contract can be constructed from standard-form hedge contracts. We draw out several lessons for policymakers.

Differentiated pricing for the retail electricity provider optimizing demand response to renewable energy fluctuations

As renewable energy expands, its fluctuations challenge grid security. Demand response (DR) is a crucial solution for ensuring grid stability. This paper designs a two-stage optimization model for the retail electricity provider (REP), aiming to develop differentiated prices to incentivize multiple types of users in DR. In the first stage, the REP utilizes energy storage to adjust electricity purchases. In the second stage, a bi-layer optimization model is constructed. The REP participates in incentive-based DR and sets differentiated prices for lower-level users. Based on these prices, multiple types of users modify their electricity consumption behavior, aiming to maximize the combined satisfaction with the comfort and economy of electricity consumption. Finally, the Karush-Kuhn-Tucker (KKT) method is used to derive the equilibrium solution between user response quantity and price. The results indicate that (1) Combining energy storage and user-side DR boosts responsiveness by 28.5% compared to storage alone and 23.2% compared to user-side DR alone. (2) Energy storage allows the REP to create more compelling prices for residential users. (3) Differentiated pricing boosts industrial users participating in DR, resulting in over 50% higher response rate than uniform pricing. This study provides policy implications for grid managers to develop effective DR programs.

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.

Technology assessment for the transition to a renewable electric grid

To reduce carbon emissions, generation of electricity from combustion systems is being replaced by renewable resources. However, the most abundant renewable sources – solar and wind – are not dispatchable, vary diurnally and are subject to intermittency, and produce electricity at times in excess of demand (excess production). To manage this variability and capture the excess renewable energy, energy storage technologies are being developed and deployed such as battery energy storage (BES), hydrogen production with electrolyzers (ELY) paired with hydrogen energy storage (HES) and fuel cells (FCs), and renewable natural gas (RNG) production. While BES may be better suited for short duration storage, hydrogen is suited for long duration storage and RNG can decarbonize the natural gas system. California Senate Bill 100 (SB100) sets a goal that all retail electricity sold in the State must be sourced from renewable and zero-carbon resources by 2045, raising the questions of which set of technologies and in what proportion are required to meet the 2045 target in the required timeframe as well as the role of the natural gas infrastructure, if any. To address these questions, this study combines electric grid dispatch modeling and optimization to identify the energy storage and dispatchable technologies in 5-year increments from 2030 to 2045 required to transition from a 60% renewable electric grid in 2035 to a 100% renewable electric grid in 2045. The results show that, by utilizing the established natural gas system to store and transmit hydrogen and RNG, the deployment of battery energy storage is dramatically reduced. The required capacity for BES in 2045, for example, is 40 times lower by leveraging the natural gas infrastructure with a concomitant reduction in cost and associated challenges to transform the electric grid.

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

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

Second-best electricity pricing in France: Effectiveness of existing rates in evolving power markets

The adoption of time-varying pricing in the French electricity market has been historically low despite their availability to consumers well before the deployment of smart metering. However, as the share of variable renewable electricity increases and carbon prices grow, the demand-side response will become increasingly important to achieve efficiency gains. Relying on the historical hourly consumption of French electricity consumers and multiple prospective weather years, we study the gain allowed by the broader adoption of time-varying electricity retail rates in low-carbon power systems. We develop a four-stage methodology to assess the efficiency and stability of the electricity supply cost component of the electricity bill in France for historical and prospective years. Our analysis demonstrates that peak pricing schemes have increasing interest in the context of further deployment of renewable capacity, capturing 25% to 50% of the welfare gain reached by real-time pricing schemes. The corresponding deadweight loss of peak pricing schemes represents 0.4 to 1 bn EUR per year in 2030 compared to real-time prices (RTP) schemes. Conversely, deadweight loss of current time-of-use (ToU) rates represents 1 to 1.2 bn EUR per year in 2025 and increases over time. The current design of TOU rates does not provide adequate incentives in future power systems. Besides the tariff efficiency, the results underlined an increasing price difference between on-peak and off-peak rates. This questions the social acceptance of time-varying retail rates for consumers unable to hedge against peak prices. By analysing the effectiveness and limitations of time-varying rates, our findings underline the importance and value of price-based DR in future power systems in general. Our results highlight the need to revise the end-user tariff design in the French power system, notably with regard to the conveyed price signals.

Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model’s robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel “policy-versus-policy” strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R2, MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF.

Unit Equivalent Distribution Network Pricing for Electricity Retail Market

Unit Equivalent Distribution Network Pricing for Electricity Retail Market

Battery recycling models selection and contractual incentives: From an information sharing perspective

As the battery recycling market in China is still in an embroynic stage, the mechanisms, channels, pricing, and efficiency of battery recycling are shrouded in opacity. Battery recycling in China is currently dominated by electric vehicle manufacturer, supplemented by other market players such as battery producers and electric vehicle retailers. Due to the lack of information, battery recycling lacks cooperation and communication, and the whole market is dealing with "Information Island." Therefore, based on information sharing and channel competition, this paper designs a Stackelberg game model with an electric vehicle manufacturer, an electric vehicle retailer, and a third-party recycler as the primary participants. This study found that effective information sharing can reduce intensified market competition and promote the transformation of market players into “collaboration state”. To promote partnership in the battery recycling market, this paper proposes a revenue-sharing contract incentive for the electric vehicle retailer to share demand information about battery recycling. It finds a revenue-sharing contract based on information sharing can help companies and competitors make appropriate behavioral choices in the battery recycling market. This study demonstrates that information sharing benefits the electric vehicle manufacturer and the third-party recycler while offering no advantages to the electric vehicle retailer. The decreasing accuracy of demand signals results in the diminishing value of information sharing for the electric vehicle manufacturer and the third-party recycler. The revenue-sharing contract can incentivize the electric vehicle retailer to share information.

Price responsiveness of solar and wind capacity demands

Accurate estimates of the price responsiveness of residential, commercial, and industrial electricity demands are essential for energy policy modelling, integrated resource planning, and determining a competitive wholesale electricity market's generation levels, prices, and capacity investments. Hence, we estimate the own-price elasticities of solar and wind capacity demands of a load serving entity (LSE) that provides retail electricity service, thereby answering two interrelated research questions: (1) does solar capacity demand far exceed wind capacity demand? and (2) are solar and wind capacity demands price-elastic? Inspired by the theory of input demand under input price uncertainty, our innovative methodology integrates (a) wholesale spot energy price forecasts by time of day; (b) pseudo data found by minimizing a LSE's annual risk-adjusted budget for procuring solar and wind capacities; and (c) econometric analysis of (b) to estimate the extent of substitutability between solar and wind capacities and the own-price elasticities of solar and wind capacity demands. Using Texas as an illustrative example, we find that when solar and wind power purchase agreements have similar energy prices, solar capacity demand is approximately four times wind capacity demand. Further, the own-price elasticity estimates are −5.34 for solar capacity demand and −5.65 for wind capacity demand. As a result, solar and wind capacity demands tend to substantially grow (shrink) in response to declining (rising) solar and wind energy prices. This lends support to proposals to raise solar and wind energy prices for mitigating the adverse effects of large-scale variable renewable energy development on an electric grid's efficient operation and system reliability. However, adopting such proposals also slows the grid's pace of decarbonization, thus underscoring the policy and regulatory challenges in the quest for a clean and sustainable electricity future. Hence, our policy recommendation of price managing solar and wind capacity demands is a topic of policy debate that deserves the attention of an electric grid's stakeholders.

Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users’ electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally.

Operative Benefits of Residential Battery Storage for Decarbonizing Energy Systems: A German Case Study

The reduction in PV prices and interest in energy independence accelerate the adoption of residential battery storage. This storage can support various functions of an energy system undergoing decarbonization. In this work, operative benefits of storage from the system perspective, namely, generation cost reduction and congestion mitigation, are investigated. Germany is chosen as a case study due to its strong reliance on variable renewable energy. For the analysis, an economic dispatch model with a high spatial resolution is coupled with a pan-European transmission grid model. It is shown that the system’s generation costs are highest when the assets are used only to maximize PV self-consumption, and the costs are lowest when the storage also reacts to the market dynamics. This amounts to a 6% cost reduction. Both operation strategies result in an equal level of grid congestion and infrastructure loading. This is improved with a strategy that accounts for regional peak reduction as a secondary objective. The high congestion level emphasizes that grid expansion needs to keep pace with the generation and electrification expansion necessary to decarbonize other sectors. Lastly, policymakers should enable multipurpose utilization, e.g., via the introduction of market-oriented retail electricity prices with intervention options for grid operators.

Electricity Retail Rate Design in a Decarbonizing Economy: An Analysis of Time-of-use and Critical Peak Pricing

Currently, the main component of most U.S. consumers’ electricity bills is based on a constant price per kWh consumed. As intermittent renewable resources and flexible loads that can be shifted within days (such as electric vehicle charging) gain prominence in the electricity system, the efficiency gains to be realized from basing bills instead on wholesale spot prices increase. There is little political support for this change, however. We focus on second-best alternatives: time-of-use (TOU) rates and critical peak pricing (CPP). We introduce alternative assessment criteria that focus on intra-day load shifting. Using historical data, we find that TOU rates can reasonably replicate the intra-day load-shifting incentives provided under spot pricing. Thus, TOU rates, especially when complemented with CPP involving load control during infrequent scarcity price events, can be considerably more socially valuable than previously estimated.

Retail electricity prices in New Zealand: recent trends and the relationship to market shares

This paper constructs a monthly price series for electricity retailers in New Zealand, broken down by region and retailer, from January 2018 until June 2023. Since 2014, there has been no such price series for New Zealand that is disaggregated by retailer. We find that, over the time period of our dataset, while average prices across the largest five retailers are consistently higher than that of the smaller retailers in our dataset, the gap has narrowed substantially. The narrowing of this price gap is associated with the following trends in market shares. While, from January 2018 to September 2021, the smaller retailers, taken as a group, made considerable gains in market share at the expense of the larger retailers, the growth in the market share of the smaller retailers in our dataset stagnated in the remainder of the time period.

Design and Pricing Analysis of Electricity Retail Package System Based on Single Pendulum Equation

The electricity retail industry is becoming increasingly competitive, and retailers are looking for innovative ways to attract and retain customers. One potential strategy is to offer retail packages that include electricity pricing based on the single pendulum equation (SPE). This article discusses the design of a retail package system for electricity retailers based on the SPE. The system utilizes real-time data on electricity supply and demand to create a dynamic pricing system that adjusts prices based on changes in the market. This article present the single pendulum equation model and data obtained from Residential Electricity End-use Demand Dataset of Costa Rica (REEDD-CR). It outlines the key steps in designing the system, including gathering data on electricity supply and demand patterns, creating a pricing algorithm based on the single pendulum equation, and integrating the system with billing and customer management systems. The article also emphasizes the importance of considering customer preferences and behavior in the design of the system. The study findings stated that single pendulum model has provided an evaluation time of 1.5s and price and demand value of 0.6. A well-designed dynamic pricing system based on the single pendulum equation has the potential to provide customers with cost savings and increased flexibility while allowing retailers to optimize their pricing strategies in a competitive market.

Disparate Regulatory Pathways for Animating Net‐Zero Legislation

State legislatures have been very active in recent years in the pursuit of net‐zero action. This column discusses the essential institutional frameworks within which U.S. regulators and their utilities have long existed. That framework—legal, legislative, accounting, and administrative—defines the relationship between U.S. regulatory commissions and their jurisdictional utilities. That “institutional ecosystem” has been highly successful: prices in the U.S. for both retail electricity and natural gas public service is a fraction of those in our peer industrialized nations.

Subsidizing the Shift to Renewable Energy in Korea: A Levelized Cost Analysis and Computation of Required Subsidies

This study addresses the pivotal challenge of transitioning from nuclear to renewable energy sources, considering the distinctive energy landscape of South Korea characterized by high land costs and high dependence on nuclear energy. Amidst global momentum toward sustainable and carbon-neutral energy, South Korea’s Renewable Energy 3020 Implementation Plan aims to achieve 20% of power generation from renewables by 2030. Shifting away from nuclear energy, however, may lead to rising energy costs, supply disruptions, and environmental ramifications. Furthermore, achieving grid parity between renewable energy and nuclear power by the late 2030s is unlikely, in contrast to earlier projections. This is primarily due to the comparatively lower production cost of nuclear energy. To expedite the shift toward renewable energy, our study suggests transitioning from the Renewable Energy Portfolio Standard (RPS) to a Feed-in-Tariff (FiT) system. This change would offer voluntary incentives for investment in renewable energy. Additionally, we compute Feed-in-Tariff subsidies required to achieve grid parity by 2030, concluding that the amount of subsidies required to reach grid parity by 2030 is 54% of retail electricity price. This research offers valuable insights for policy formulation and energy transition planning.

Profitability landscapes for competitive photovoltaic self-consumption

Photovoltaic self-consumption (PVSC) is a central element of the energy transition. However, retail electricity tariffs have not adapted to the emerging phenomenon of demand-side generation. We introduce the concept of profitability landscapes to evaluate the effects of a cost-reflective retail tariff and a competitive PVSC regulation on prosumers’ returns depending on system costs and self-consumption share. The trade-off between these two elements reveals the value of demand flexibility. We find that PVSC would be profitable in most European countries when external costs are internalized, confirm the diminishing marginal returns on demand flexibility and quantify the effects of prices, insolation and profitability on the value of demand flexibility.

Load Balance Forecasting Based on Hybrid Deep Neural Network

Load forecasting is the foundation of utility design, and it is a fundamental business problem in the utility industry. Load forecasting, mainly referring to forecasting electricity demand and energy, is being used throughout all segments of the electric power industry, including generation, transmission, distribution, and retail. In this paper, a long short-term memory network with a hybrid approach is improved with a dense algorithm and proposed for electricity load forecasting. A long short-term memory network is designed to effectively exhibit the dynamic behavior of load time series. The proposed model is tested for Panama study including historical data and weather variables. The prediction accuracy is validated by performance metrics, and the best of the metrics are attained when mean absolute error is 5.262, mean absolute percentage error 0.0000376, and root mean square error 18.243. The experimental results show a high prediction rate for load balance forecasting of electric power consumption.

Electricity-carbon coupling retail package optimization considering low-carbon benefits of distributed renewable energy

Distributed renewable energy has developed rapidly in recent years, but its low-carbon value is usually ignored due to its limited individual capacity. It will be an important business for electricity retailers to aggregate these distributed renewable resources and make use of their carbon emission reduction benefits. Therefore, this paper proposes a business model for electricity retailers based on electricity-carbon coupling retail packages. Firstly, a framework of the business model is proposed to support distributed renewable energy transaction among the electricity retailer, prosumers, and consumers. Secondly, dynamic carbon evaluation mechanism-based electricity-carbon coupling retail packages are designed, which contain three types of generation packages for prosumers (e.g., fixed subsidy package, dynamic subsidy package and daytime generation package) and three types of consumption packages for consumers (e.g., daytime consumption package, nighttime consumption package and minimum consumption package). In addition, a single-leader and multi-followers Stackelberg game model is constructed to optimize the designed packages. Especially, the electricity retailer aims at maximizing its profit considering conditional value at risk (CVaR) as the leader. Meanwhile, prosumers and consumers respectively aim at maximizing their profit satisfaction and comprehensive satisfaction as followers. Finally, the particle swarm optimization (PSO) algorithm and CPLEX commercial solver are both used to obtain the model's Nash equilibrium (NE). Example analysis verifies the designed electricity-carbon coupling retail packages can utilize the low-carbon value of distributed renewable resources and improve the benefits and satisfaction of prosumers and consumers.