Retail Electricity Research Articles

A Framework for Several Electricity Retailers Cooperatively Implement Demand Response to Distributed Data Center

A distributed data center cluster (DDCC) consists of data centers that are geographically dispersed while managed by the same company. A DDCC is composed of several data centers in different regions, so it purchases electricity from several electricity retailers in different regions. Commonly, there is a lack of cooperation between the several electricity retailers. In this paper, a cooperation framework where several electricity retailers cooperatively implement incentive-based demand response (DR) to the same DDCC is proposed. Firstly, several electricity retailers which power for the same DDCC together publish their DR instructions to the DDCC. The DR instructions are analyzed in this paper. Then a novel DDCC energy management model is proposed for the DDCC to participate in the collaborative DR. Finally, a reasonable profit distribution mechanism is adopted for the several electricity retailers and the DDCC to allocate the entire profit of the collaborative DR. The case study verifies that the proposed cooperation framework can maximize the whole benefit of the DDCC and the several electricity retailers meanwhile ensures that the profits obtained by each member (the DDCC and the several electricity retailers) from the collaborative DR are not fewer than that of the independent operation.

Investment strategy for renewable energy and electricity service quality under different power structures

<p style='text-indent:20px;'>This paper investigates and develops an electricity supply chain with an electricity generator and electricity retailer. The electricity generator invests in renewable energy and sells the electricity to the electricity retailer; the retailer then sells the electricity to consumers according to electricity service quality. Three models are considered: the vertical Nash game, the electricity-generator Stackelberg model, and the electricity-retailer Stackelberg model. Finally, the optimal solutions under different power-structure models are compared. Certain conclusions can be drawn from the findings: (1) the highest electricity service quality and renewable energy investment are obtained in the vertical Nash game model, the electricity generator obtains more profits in the electricity-generator Stackelberg model, and the electricity retailer obtains more profits in the electricity-retailer Stackelberg model. (2) The investment in renewable energy in the electricity-generator Stackelberg model is less than that in the electricity-retailer Stackelberg model, and the investment in electricity service quality in the electricity-retailer Stackelberg model is less than that in the electricity-generator Stackelberg model (under certain conditions). (3) Both increasing the electricity service quality sensitivity and renewable energy sensitivity could increase the optimal electricity price, electricity demand, electricity service quality, renewable energy investment, and supply-chain enterprises' profit.</p>

Dual-channel supply chain coordination strategies considering electricity quality

<p style='text-indent:20px;'>The introduction of tradable electricity platforms in China has enriched the channels for consumers to purchase electricity and intensified the competition among channels. The quality of electricity is an essential factor affecting consumer purchasing behavior. This paper mainly focuses on the influence of the competition among channels on electricity quality.We construct a dual-channel electricity supply chain, in which electricity generators sell electricity directly to consumers or resell it through electricity retailers. We use the backward induction method of game theory to solve the equilibrium solutions in centralized, decentralized, and coordinated decisions. By analyzing and comparing the equilibrium solutions, we draw the following conclusions; (1) Electricity quality, electricity price, electricity demand, and profits increase with the sensitivity coefficient of electricity quality and price competition coefficient. (2) Electricity quality, electricity price, electricity demand, and profits decrease with the investment cost coefficient of quality improvement. (3) The increase in transmission and distribution costs reduces the electricity quality while increasing the electricity price through both channels. Furthermore, the increase leads to an increase in electricity demand and the profit of the electricity retailer through the resale channel. However, it reduces the electricity demand and the profit of the electricity generator through the direct sales channel. (4) A combined contract consisting of revenue sharing and quality improvement investment cost-sharing can lead to coordination.</p>

Ценовые параметры поставки электроэнергии как базис управления спросом на электропотребление в регионе

Energy cost management (more than 45 % in the structure of total costs) can significantly reduce own energy supply costs and improve operational efficiency of an enterprise. The study examines regional electricity price parameters, affected by retail and wholesale electricity markets, in terms of the possibilities for industrial enterprises to implement price-dependent demand management to reduce energy purchase costs. The article aims to distribute Russian regions according to the prospects for reducing energy purchase costs by managing electricity demand schedules. The following methods were utilised: statistical analysis of hourly average electricity prices per year, month and day in the regional context; mathematical modelling and calculation of authors’ coefficients of average electricity prices and coefficients of daily price volatility for assessing prospects for effective energy demand management in Russian regions; construction of positioning maps for grouping and identifying regions where the implementation of price-dependent demand management is possible. Electricity price parameters of all Russian regions were examined. The paper analysed principles of electricity pricing for domestic industrial enterprises, researched the impact of price volatility on energy purchase costs, assessed the contribution of electricity costs to total energy consumption costs of enterprises. Coefficients of average electricity prices, coefficients of daily price volatility and coefficients of efficiency of price-dependent electricity consumption were applied to study electricity price parameters in the regional context. As a result, the article presented a map of electricity price parameters, where regions are grouped according to the possibility of implementing demand management mechanisms. Additionally, specific practical recommendations on price-dependent demand management for electricity consumption of industrial enterprises were given for each identified regional group.

Operation Cost Optimization Method of Regional Integrated Energy System in Electricity Market Environment Considering Uncertainty

In the electricity market environment, the regional integrated energy system (RIES) can reduce the total operation cost by participating in electricity market transactions. However, the RIES will face the risk of load and electricity price uncertainties, which may make its operation cost higher than expected. This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty. Firstly, based on the operation cost structure of the RIES in the electricity market environment, the energy flow relationship of the RIES is analyzed, and the operation cost model of the RIES is built. Then, the electricity purchase costs of the RIES in the medium- and long-term electricity markets, the spot electricity market, and the retail electricity market are analyzed. Finally, considering the risk of load and electricity price uncertainties, the operation cost optimization model of the RIES is established based on conditional value-at-risk. Then it is solved to obtain the operation cost optimization strategy of the RIES. Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy, constrain the risk of load and electricity price uncertainties, and help balance the risks and benefits.

Estimating the Price Elasticity of Natural Gas and Electricity in Pacific Northwest

This paper estimates the price elasticity of electricity and natural gas in the Pacific Northwest. Electricity and natural gas are substitutes; and demands of different classes of consumers for the energy are price-inelasticity. The results are when the price of retail electricity and natural gas changes, consumers would choose the cheaper one to make up for the increased cost of another energy price. Consumers’ demand for energy is mainly stable, they won’t affect a lot by the change in energy prices.

Competition Development in Retail Electricity Markets: Opportunities for Improving the Russian Laws on the Electric Power Industry

Taking measures to develop competition and improve market relations is of paramount importance for the functioning of the electric power industry. This is due to the implementation of the strategic tasks set for the industry to improve the reliability and quality of power supply to consumers, the need to achieve the energy security goals of the Russian Federation, and the role of the electric power industry in strengthening the national economy. Currently, retail markets are highly concentrated and have poor competition due to a number of problems that have not yet been resolved. The Russian Ministry of Energy has prepared a promising model of competitive retail electricity markets that establishes basic requirements for a promising model of these markets, as well as the areas where competitive relations should be developed. The implementation of the underlying principles of the concept requires amendments to Russian laws on the electric power industry. To this end, we propose to consider drafting a federal law and a decree of the Government of the Russian Federation in furtherance thereof.

State management of rural electricity in Phu Tho province

Electricity is an essential factor for the swift development of a country’s socio-economic status. For rural areas, electricity plays an even more important role in changing the face of rural areas, helping to gradually narrow the gap between rural and urban areas. This study uses both primary and secondary data collection methods. The primary data comes from a survey of 50 managers and employees of the Phu Tho Electricity company. The secondary data comes from a survey of 120 households in the province that use electricity. Research findings suggest that the management of rural electricity prices has been completed, including the establishment of a price bracket to control and limit abnormal price increases (the price bracket for the average retail electricity price ranges from 1,490 VND/Kwh to 1,582 VND/Kwh); gradually implementing advanced types of rural power management, such as rural cooperatives, etc. However, there are existing barriers to the state's management of rural electricity in Phu Tho province. These include the province's wide area, the traversal of power lines across hilly terrain with numerous trees and other obstacles, and the fact that the landscape shifts shape every time it rains, floods, or is hit by a tornado. In instance, the energy service cooperatives that oversee the rural low-voltage grid have not invested in upgrades in many years. As a result, electrical service continues to deteriorate. The purpose of this research is to examine the power structure of state management of rural power in the province. Therefore, this paper recommends and discusses potential options to strengthen the role of state management of rural power in Phu Tho province in the future.

Optimal Strategies of Power Generation Companies and Electricity Customers Participating in Electricity Retailing Trading

Optimal Strategies of Power Generation Companies and Electricity Customers Participating in Electricity Retailing Trading

Insuring a Small Retail Electric Provider’s Procurement Cost Risk in Texas

Motivated by the relatively infrequent but very large price spikes in the day-ahead and real-time energy markets operated by the Electric Reliability Council of Texas, this paper proposes an insurance that a small and risk-averse retailer in Texas (i.e., a retail electric provider (REP)) may buy to prevent financial insolvency caused by inadequate risk management. It also demonstrates the insurance’s practical design, pricing, and implementation. As participation in the REP’s procurement auction is voluntary, the insurance is mutually beneficial for the REP and the insurance seller. Hence, the proposed insurance is a newly developed wholesale market product that deserves consideration by REPs in Texas and competitive retailers elsewhere.

Screening for partial collusion in retail electricity markets

Starting from a theoretical model of coalition formation, we analyse which price markers are more adequate to identify clues of partial cartels. Based on the theoretical model, we argue that measures of the shape of a distribution (such as kurtosis and skewness) can complement centrality measures (such as the mean and the dispersion of the distribution of prices) to screen for potential partial cartels. We apply these price markers to price data from the Norwegian retail electricity market.

Low-dimensional representation of monthly electricity demand profiles

This paper addresses the problem of reducing the number of values required to characterize an electricity demand profile, which is usually known as its dimensionality. This reduction may have a significant impact on the computational efforts and storage capacities required to analyze and process high volumes of electricity load curves. Also, the reduction to 2 or even 1 component enables its graphic representation. Specifically, this work is mainly focused on profiles defined by their monthly demand values, and where the clients are aggregated by locations and/or economic activities. This approach is of great interest for marketing analysis and decision-making of electricity retailers. In this sense, the use of dimensionality reduction techniques based on knowledge (calendar and temperature) along with the application of data-driven procedures (Principal Component Analysis and autoencoders), are explored in the paper. The results of this research show that autoencoders clearly outperform the other techniques, yielding errors in the reduction process between 15% to 40% lower and preserving distances between profiles in the low-dimensional spaces, with a correlation of 0.93 with the distances in high dimensional space. Additionally, the bidimensional graphical representation of a profile can easily be interpreted in a polar way, where the angle denotes the shape of the profile, and the radius reveals its scale. To reach these results, a very large dataset has been employed, with about half a million aggregated profiles corresponding to the electricity consumption during 3 years of more than 27 million clients in Spain.

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.

Sustainable opportunities to recover power plants’ waste heat: a benchmark of techno-economically optimized heat pumps

Current climate targets, adopted worldwide, pursue the decarbonization of all energy sectors. In power generation, the spread of RES has caused the shift of the Combined Cycle Gas Turbine (CCGT) power plants’ traditional role from providing constant baseload power to flexible operations providing services and backup capacity to the grid, as a consequence, the revenues opportunities on the traditional electricity markets (e.g., day-ahead, and intra-day markets) are no longer certain. At the same time is urgent to address a significant carbon intensity reduction in the heating sector, which is responsible for a large part of the current greenhouse gas emissions. Exploiting the low-exergy waste heat of CCGT’s water-cooled condenser as a thermal source for vapor compression heat pumps can meet two targets. On one hand, the revenues from heat production enhance the viability of the CCGT power plant, often essential for the security of the electrical supply. On the other hand, the heat delivered to the user represents a low-carbon alternative to traditional gas-fired boilers or even to air-sourced heat pumps characterized by lower efficiency. This work assesses the recovery of thermal energy from the sea cooling water at the bottoming cycle condenser of Tirreno Power 794 MWel CCGT in Vado Ligure through a high-temperature heat pump using R600 as a working fluid. Cooling water represents a privileged heat source and the integration into the power plant’s site allows running the HP at a lower cost than the electricity retail price. The case study takes into account the new Vado Ligure sports hall, to size the HP and explore the economic and environmental performances. The proposed layout is compared with the main solutions available: air source heat pump, gas, and electric boilers. The results show that on a lifespan of 20 years such integration of a heat pump with a CCGT could lead to consistent economical savings and emissions cuts.

Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design

Coupon Incentive-based Demand Response (CIDR) is a novel type of demand response mechanism targeting at small residential end-consumers, and has advantages over traditional Demand Response (DR) programs in its adaptability to most electricity retail plans, voluntary and without penalty. CIDR’s three-layer mechanism was modeled with Stackelberg Game in the authors’ previous studies, and its Nash Equilibrium is usually obtained by iterative solution. However, there is limited study on the efficiency and social welfare of the traditional CIDR mechanism. This paper provides the following contributions: (1) Introducing the response function of consumers and Load Serving Entities (LSEs) considering bounded rationality, and modeling the demand response resources as Virtual Power Plants (VPPs) to find the analytical solution; (2) By comparing the response function with centralized dispatch, this paper reveals the social welfare loss under the Nash Equilibrium of current CIDR mechanism; (3) An Extra Incentive Term (EIT) awarded to the LSE was designed to maximize the social welfare, and its form and parameters are determined under different conditions such as multiple DRs and LSEs, as well as congested power system. The case study shows that EIT has successfully helped to improve the social welfare as well as increased the retail saving and suppressed Locational Marginal Prices in most scenarios.

Hierarchical control strategy of electric vehicles with demand response in retail electricity markets

Hierarchical control strategy of electric vehicles with demand response in retail electricity markets

Optimal real-time pricing and electricity package by retail electric providers based on social learning

As the global retail electricity market liberalizes, retail electric providers (REPs) are optimizing pricing schemes and electricity packages (EPs) to meet consumers' individual and diverse electricity plans while reducing volatility risk and ensuring profitability. However, consumer social learning (SL) helps frequently adjust electricity plans as social media evolves, preventing REPs from accurately understanding consumer electricity demand. Therefore, this paper constructs a dynamic game model of the retail electricity market considering social learning. REPs announce price and electricity package information to consumers based on market information and historical experience, and consumers first shared electricity usage plans based on their willingness to learn and share information among themselves, and then respond to REPs based on current information and experience. The main findings of this paper are as follows: (i) social learning increases consumer utility, promotes competition, depresses REPs prices, shrinks price differentials, and induces concessions by REPs, but raises volatility risk. (ii) electricity packages combined with social learning can save electricity consumption and significantly reduce volatility risk. Finally, this paper uses Smart Meter Energy Consumption data in London households to demonstrate the impact of social learning and to provide decision support for REP's electricity sales strategies.

Residential customers-oriented customized electricity retail pricing design

As the retail market becomes more competitive, it is significant for retailers to present a proper and attractive retail pricing strategy. This paper proposes a two-stage customized retail pricing design based on personalized demand response incentives for different residential users. To accurately simulate the load variation according to the price change, the real-time elasticity concept has been first introduced into the framework of customizing retail prices. The utilization of customizing prices is to make better use of the price elasticity to encourage the load shifting of end-users and their active engagement in demand response. In the first stage, the retail price base value is obtained by simultaneously optimizing the time-of-use (ToU) structure and corresponding price level. The second stage focuses on the load redistribution caused by individualized price incentives, and the retail price revision is set. The two-stage solution results are combined to obtain the final customized retail electricity price plans. To verify the feasibility and effectiveness of the proposed method, the customized retail pricing scheme is tested with 30 selected users. Compared with the traditional unified retail pricing method, the total retail profit increases by 10.62%, and the total retail conditional value at risk (CVaR) and consumers’ payment decrease by 27.69% and 3.82%, respectively through the proposed customizing retail price method. The numerical results prove that through the proposed retail pricing method the retailing profit has increased, while the consumers' payment and risk management have also been guaranteed.

A Review of Electricity Tariffs and Enabling Solutions for Optimal Energy Management

Today, electricity tariffs play an essential role in the electricity retail market as they are the key factor for the decision-making of end-users. Additionally, tariffs are necessary for increasing competition in the electricity market. They have a great impact on load energy management. Moreover, tariffs are not taken as a fixed approach to expense calculations only but are influenced by many other factors, such as electricity generation, transmission, distribution costs, and governmental taxation. Thus, electricity pricing differs significantly between countries or between regions within a country. Improper tariff calculation methodologies in some areas have led to high-power losses, unnecessary investments, increased operational expenses, and environmental pollution due to the non-use of available sustainable energy resources. Due to the importance of electricity tariffs, the authors of this paper have been inspired to review all electricity tariff designs used worldwide. In this paper, 103 references from the last ten years are reviewed, showing a detailed comparison between different tariff designs and demonstrating their main advantages and drawbacks. Additionally, this paper reviews the utilized electricity tariffs in different countries, focusing on one of the most important countries in the Middle East and North Africa regions (Egypt). Finally, some recommended solutions based upon the carried-out research are discussed and applied to the case study for electricity tariff improvement in this region. This review paper can help researchers become aware of all the electricity tariff designs used in various countries, which can lead to their design improvements by using suitable software technologies. Additionally, it will increase end-users’ awareness in terms of deciding on the best electricity retail markets as well as optimizing their energy usage.

Prescriptive selection of machine learning hyperparameters with applications in power markets: Retailer’s optimal trading

We present a data-driven framework for optimal scenario selection in stochastic optimization with applications in power markets. The proposed methodology relies on the existence of auxiliary information and the use of machine learning techniques to narrow the set of possible realizations (scenarios) of the variables of interest. In particular, we implement a novel validation algorithm that allows optimizing each machine learning hyperparameter to further improve the prescriptive power of the resulting set of scenarios. Supervised machine learning techniques are examined, including kNN and decision trees, and the validation process is adapted to work with time-dependent datasets. Moreover, we extend the proposed methodology to work with unsupervised techniques with promising results. We test the proposed methodology in a realistic power market application: optimal trading strategy in forward and spot markets for an electricity retailer under uncertain spot prices. The results indicate that the retailer can greatly benefit from the proposed data-driven methodology and improve its market performance. Moreover, we perform an extensive set of numerical simulations to analyze under which conditions the best machine learning hyperparameters, in terms of prescriptive performance, differ from those that provide the best predictive accuracy.