Retail Electricity Research Articles

Carbon emission reduction dynamic decision-making in the electricity supply chain with different carbon emission allowance principles

To address the impacts of typical principles for allocating initial carbon emission allowance, namely, the grandfathering and benchmarking principles on long-term emission reduction strategies, this paper presents four differential game models for an electricity supply chain consisting of a single electricity supplier and a single electricity retailer. The equilibrium solutions for different models are obtained and comparatively analyzed. The results show that the power supplier exhibits greater emission reduction efforts and, therefore, that the retailer has higher profits under the benchmarking principle than under the grandfathering principle. Moreover, under the two principles, the level of the power supplier’s emission reduction efforts is related to the decision-making modes of the electricity supply chain. Under specific conditions, the power supplier’s profit is also higher under the benchmarking principle than under the grandfathering principle. In addition, this study extends the models to investigate the constrained differential game models and obtains the game equilibrium solutions, which can be proved as saddle point equilibria.

The cost of CO2 emissions abatement in a micro energy community in a Belgian context

This paper investigates and quantifies the benefits and the cost of CO2 emissions abatement of a Micro Energy Community (MEC) in a tiny residential cluster of three houses in a Belgian context. A simulation-based comparative analysis is performed of two individual and two MEC concepts, i.e. a reference scenario, an individual electrification scenario, an electricity sharing scenario, and an energy community scenario. A deterministic dynamic white-box modelling approach, including optimal control, is used, considering heat for space heating and domestic hot water, and electricity for electrical appliances. The energy system that achieves the greatest emission reduction at the lowest cost, has a collective heat pump and a photovoltaic installation sized based on the plug load demand. This system reduces the annual CO2 emissions by 11.48 tons at a cost of 179 EUR/ton CO2 considering 2021 dynamic retail electricity prices with a median of 251 EUR/MWh and a fixed retail gas price of 64 EUR/MWh. However, the results are strongly dependent on the gas-electricity price ratio. The highest value of the retail gas price in 2021 leads to a negative CO2 emissions abatement cost of 154 EUR/ton CO2, putting energy community concepts on the radar for a cost-effective energy transition.

Categorical claims about voluntary renewable energy actions are not supported by categorical evidence

Abstract Voluntary renewable energy procurement by retail electricity customers could play a critical role in achieving clean energy objectives. Research could optimize that role by providing accurate and nuanced information about how different voluntary actions most effectively drive renewable energy deployment. Recent literature has made categorical assertions that broad classes of voluntary actions are weakly or not impactful. This Perspective argues that these categorical assertions are not supported by equally categorical evidence. A closer examination shows the evidence is less conclusive and less generalizable than stakeholders may presume. Voluntary renewable energy buyers would benefit from more nuanced analysis of the impacts of different energy procurement actions across distinct contexts.

Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-Ahead Electricity Prices Using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Ensemble Learning

Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants.

Theoretical study on demand-side management to reduce imbalance between electricity supply and demand

The gap between electricity supply and demand, which is the electricity imbalance, can have negative economic impacts on retail electricity suppliers. This paper presents a methodology for designing demand response (DR) programs while mitigating the negative impacts. The DR programs require the retailers to set target electricity consumption and prices of economic incentives for customers who cooperate with the DR programs. First, the target electricity consumption is determined by solving a problem of social welfare maximization in which the objective function is the sum of economic surplus of the retailers and their customers and impacts of electricity imbalance. The optimal electricity prices or the optimal rebate levels are then calculated under the principle that rational customers act to maximize their economic surplus. Through numerical simulations, the authors confirmed that the proposed methodology mitigated the impacts of electricity imbalance. Meanwhile, results of the numerical simulations showed that in practical situations, there were cases where the retailers unable to reduce the electricity imbalance profitably. Therefore, the authors additionally discuss ways to assess the electricity imbalance, which motivates the retailers to reduce the imbalance, in such cases.

Green electricity product menu design for retailers without knowing consumer environmental awareness

Adverse selection problem due to asymmetry of consumer environmental awareness (CEA) could reduce electricity retailer's profit and obstructs renewable energy (RE) consumption. This study analyzes mechanism design problem of retailer constricted by RE consumption quota to incentive consumers display true CEA in competitive retail market. Based on principal-agent theory, we develop an extended principal-agent model with the addition of an RE consumption quota constraint to design a green electricity product menu which includes the green level, the price and electric quantity. Influence of information symmetry and quota setting on product attributes, retailer's profit, consumer utility, RE consumption is analyzed, and results show that: (1) Compare to symmetric information, under asymmetric information, green level of product provided to consumers with CEA (denoted as C1) distorts upward, information rent is greater than zero when quota is large, while green level of product provided to consumers without CEA (denoted as C2) distorts downward, with no information rent. (2) Under asymmetric information, unit environmental premium (UEP) of green electricity positively relates to proportion of C1, CEA level, and incremental WTP, and reaches the maximum value at a certain quota. (3) Under asymmetric information, retailer's profit and RE consumption positively relates to proportion of C1, and CEA level. (4) Under asymmetric information, quota difference could contribute to improve retail service quality under competitive retail market, and RE consumption elevation.

Price and carbon emission reduction technology competition in the electricity supply chain based on power structure

This study considers the research on electricity pricing and carbon reduction technology decisions of electricity supply chain enterprises under different power structures, and constructs a two-level electricity supply chain game model composed of two electricity producers and a single electricity retailer. By comparing the equilibrium solution results, the main research results are as follows: (1) In the market structure where electricity producers and electricity retailer make decisions at the same time, electricity producers are more willing to invest in low-carbon technologies and increase carbon emission reduction. The electricity retailer also set the lowest electricity prices at this time, and electricity demand is the highest. (2) In the case of the Stackelberg game for electricity producers, the wholesale electricity price is the highest, and the electricity producers obtain the most profits. In the case of the Stackelberg game for electricity retailer, the electricity price is the highest, and the profit of the electricity retailer is the highest. (3) In the market structure dominated by electricity retailer, the electricity price is the highest, and the electricity producers reduce the investment in carbon emission reduction technology after observation; the carbon emission reduction intensity is the least, and the electricity demand is also the least. (4) With the increase of price competition coefficient, the carbon emission reduction intensity, electricity demand, electricity producers and electricity retailer profit increase. However, as the competition coefficient of carbon reduction technology increases, these equilibrium solutions decline. We verify the above conclusions using multi-agent simulation.

Impact of variable electricity price on heat pump operated buildings

Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters

The adoption of smart meters and dynamic pricing programs is rapidly increasing among electric utility companies. In “Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters,” Keskin, Li, and Sunar analyze how utility companies should use smart meter data for better pricing decisions. Utility companies typically have access to consumption patterns and high-dimensional features on customer characteristics and exogenous factors. The authors identify that such feature data can exhibit different forms of heterogeneity—over time and over customers. They show that the different forms of feature heterogeneity significantly worsen the best profit performance that can be achieved by a data-driven dynamic pricing policy. The authors also develop a policy based on joint spectral clustering and contextual dynamic pricing and prove that this policy achieves near-optimal profit performance.

Does Vertical Integration Always Reduce an Electricity Retailer’s Fixed Price Offer?

We use a profit-maximizing model of forward pricing, which revealed that an electricity retailer’s fixed price offer (FPO) contains a large forward premium that encourages vertical integration of a generation company and a retailer. However, vertical integration does not always reduce FPOs, particularly when residential customers are segmented by consumption size and price sensitivity. Hence, a proposed merger of a big generation company and a big retailer requires regulatory scrutiny.

Finding opportunity in economic power dispatch: Saving fuels without impacting retail electricity prices in fuel-producing countries

In many oil- and natural gas-producing countries, fuels are allocated to the electricity sector at administratively set prices below international market prices for domestic policy reasons. This article shows that dispatching power units based on the opportunity costs of domestically used fuels can yield significant efficiencies, while end-user electricity prices can continue to reflect administratively set fuel prices. In addition, opportunity cost dispatch can bring environmental benefits when it results in switching the priority of oil and gas units in the merit order and facilitating investments in renewable energy. Therefore, this approach can support long-term net-zero emission targets. It can also resolve cross-border electricity trading issues faced by fuel-rich countries, which are reluctant to base their electricity exports on domestic fuel prices.

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.

Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid

AbstractThis study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

Assessment of Economic Viability of Direct Current Fast Charging Infrastructure Investments for Electric Vehicles in the United States

As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in the U.S. using two case studies and exploring different operational strategies including sole ownership and collaborative ventures with public and private entities. The results indicate that based on the current adoption and utilization rates in the U.S., the business model involving an owner-operator collaborating with a public partner ensures profitability and protects the investment in DCFC stations from financial losses. The study also highlights that demand charges and electricity retail prices are the factors that affect the profitability of a DCFC station.

Individual and cluster demand response in retail electricity trading with end-users in differentiated oligopoly market: A game-theoretical approach

The characterization of the competitive interdependence among power consumers in power trading system contributes to pursuing efficient power supply and suppressing peak–valley load differences. Even though Stackelberg–Nash game approach has been applied to retail electricity trading with a single utility company and a group of end-users under competitive interdependence, the extension to multi-cluster retail electricity markets is not focused yet and the best strategies of end-users are still decoupled to each other. To bridge this gap this paper investigates the price-based demand response problem in multi-cluster retail electricity markets to coordinate the energy consumption behavior of the end-users and the retail price behavior of the utility companies. Adopting the structure of hierarchical game theory, the cluster retail price is controlled by the utility company to affect the energy consumption demand of end-users within the corresponding cluster and hence increase the income of both the utility companies and the end-users. In order to describe the coupled competition relationship among the bottom end-users in the system, the Cournot competition model of the differentiated oligopoly market is constructed by taking energy consumption as the individual strategy of end-users. The existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system are proved, and a seeking algorithm is proposed to seek the Stackelberg Nash equilibrium, which is regarded as the extension from the existing algorithm for multi-cluster setup. This work gives an insight to theoretically check whether the utility companies need to redesign the contract with the power generator to ask for larger maximum supply.

Solar surge and cost shifts: Heterogenous effects of redistribution in the electricity bills in Japan

This study investigates the impact of daily intermittency in solar energy on retail electricity bills, focusing on the “Duck Curve” and “Peak Shifting” phenomena and their varying effects on different consumer groups. Notably, commercial and industrial users exhibit higher demand around midday, while residential users peak during morning and evening hours. This prompts a crucial question: do peak shifting and redistribution effects differ among consumer groups due to their distinct electricity consumption patterns? Our analysis centers on Japan's power system, which has witnessed significant solar energy penetration, with solar capacity growing from 42 GW in 2016 to 83 GW in 2022. Utilizing monthly electricity consumption data and employing robustness checks through instrumental variable methods, we empirically demonstrate that increased solar penetration leads to higher electricity prices for residential consumers but reduced costs for commercial-industrial users. Additionally, we uncover the mediating role of pumped hydro storage (PHS) stations in stabilizing prices during peak and off-peak periods by managing energy demand variability, thereby mitigating economic impacts across consumer groups. This study highlights the issue of cross-class subsidization, where certain groups bear disproportionately higher electricity costs due to increased renewable energy penetration. It underscores the necessity for carefully crafted policy interventions to uphold market fairness and sustainability alongside large-scale renewable penetration, ensuring a balanced and equitable energy transition.

Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects

Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects

System-wide benefits of temporal alignment of wholesale–retail electricity prices

Exposing residential consumers to real-time pricing (RTP) can yield significant efficiency gains, but may also pose challenges due to the inherent complexity. Using an extensive spectrum of temporal granularities for retail electricity pricing, we analyze to what extent these capture the benefits of RTP while concurrently considering the influence of three distribution tariff types in distorting the pricing signal. Via a mixed complementarity model, we consider both operational and investment decisions at retail as well as wholesale levels, accounting for their interdependencies. Our analysis reveals three key findings. Firstly, we find that decreasing temporal granularity of retail electricity prices increases total system cost due to an increasingly inefficient generation mix, however, three-hourly or six-hourly pricing can approximate RTP benefits. Secondly, decreasing temporal granularity of retail electricity prices raises average offtake and lowers average injection prices for consumers due to changes in wholesale market prices as well as consumption patterns of consumers. Thirdly, capacity-based tariffs reduce system offtake peaks and associated price increases by incentivizing battery discharge during peak consumption while volumetric tariffs, over-incentivizing solar PV investments, result in highest offtake prices and injection peaks.

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.

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.