Restructured Electricity Market Research Articles

Cost-Effective Target Capacity Assessment in the Energy Transition: The Italian Methodology

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained.

Advances and Hurdles in Solar Energy in Rajasthan

Renewable energy is a sustainable and clean source of energy derived from nature. These technologies have long been recognized as an important part of the solution to address energy security concerns and ensure economic growth in an environment friendly manner. Deregulation has changed the traditional mission and mandates of power utilities in complex ways, and had large impacts on environmental, social, and political conditions for any particular country like India. The renewable energy based power generating systems can play a major role towards the fulfilment of energy requirements of restructured electricity market. In this paper, efforts have been made to summarize the availability, current status, environmental effects, promotion policies and future potentials & strategies of renewable energy options in India. Keywords— renewable energy based power plants, renewable energy sources, renewable policy, renewable tariff.

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.

Multi-agent based modeling and learning approach for intelligent day-ahead bidding strategy in wholesale electricity market

This work considers a restructured electricity market where the agents are considered as entities, and each agent has a learning capability. Also, the Independent System Operator (ISO) adopted the nodal pricing technique for managing transmission line congestion. In this multi-agent platform, the objective of each Generating Company (GenCo) agent is maximizing its net earnings. GenCo exhibits economic capacity withholding to enhance its daily net income ports updated cost parameters to the ISO. The basic model free policy-based reinforcement algorithms fail to calculate the probability that the rewards obtained are negative or zero. To overcome this drawback, the co-learning agent-based modeling approach implements an interactive learning algorithm applied to GenCo’s for reward consideration in all respects and thereby applies the strategic approach in improvising marginal pricing, commitments, and net incomes. The co-learning effect of GenCo is accomplished with the optimum value of the learning parameters by deploying a novel interactive Variant Roth – Erev (VRE) Reinforcement learning technique for efficient electricity market operation. The introduction of various parameters in learning helps to improve the mutual interaction among the agents and provides better experimentation implementable by GenCo’s meeting the social welfare and smart bidding. The result presented in this paper suggests that the learning capability of the agents is having an impact on the marginal pricing, which supports the operator in managing the congestion in the transmission line. The analysis showcases the effectiveness of the co-learning approach adopted by the agents in the electricity market which is tested on a standard IEEE system and the performance of the technique is validated on a realistic Indian grid network.

How are public utilities responding to electricity market restructuring and the energy transition? Lessons from Israel

Public utilities in the electricity sector globally face increasing competition in the power generation segment, specifically considering government liberalization reforms that facilitate the clean energy transition process. Although the utilities may significantly impact this process, the literature has thus far neglected the perceptible strategies they employ to cope with the emerging competition, particularly with regard to renewable energy resources. This study addresses the lacuna by focusing on the case of Israel Electric Corporation (IEC), demonstrating how the coping strategies employed by this public utility evolved over time and thus shaped the local electricity sector.

Integrating Risk Preferences into Game Analysis of Price-Making Retailers in Power Market

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, retailers’ risk preferences will impact their price bidding strategies. To examine the effects of retailers’ risk preferences on their strategies and equilibrium outcomes in the retail market, an equilibrium model for price-making retailers is proposed by employing the mean–variance utility theory to model the risk preferences of retailers. The market share function is used to characterize consumers’ price-elasticity and switching behavior in the retail market. Few works in the literature address the issue of bidding strategies of retailers with different risk preferences in the electricity market with switchable consumers. Moreover, the existence and uniqueness of the Nash equilibrium are theoretically proved. A theoretical analysis is presented to investigate the impacts of wholesale price uncertainty and retailer’s risk preference on the bidding strategy. By adopting the nonlinear complementarity approach, the proposed game model is transformed into a set of nonlinear equations, which is further solved by the Levenberg–Marquardt algorithm. Finally, examples are included to verify the effectiveness of the proposed theory, and the results show that the bidding price of a retailer will increase with the increasing uncertainty of the wholesale price and the increasing risk-averse levels of itself and its rivals.

Price freezes and gas pass-through: an estimation of the price impact of electricity market restructuring

This article examines the impact of market restructuring on retail prices, using the restructuring of the electricity industry as a case study. Utilizing synthetic control as an estimation strategy, this paper finds retail competition reduced retail prices across all sectors by an average of $1.5/MWh, relative to their counterfactual outcome. On average, prices fell for residential and commercial users and rose for industrial users. The price differential is consistent with the enactment of price ceilings and increased pass-through of changes in the price of natural gas.

A coordinated model for self‐scheduling and market participation of a large consumer in the face of a comprehensive set of uncertainties with incomplete Data

AbstractIn a restructured electricity market, a large consumer could supply its electricity consumption through its own available resources or purchasing from the electricity markets. Moreover, it could participate in demand response programs (DRPs) and also supply some portion of its demand from bilateral contracts. In this regard, the large consumer's objective is to minimize its own operation cost while facing several uncertainties that could affect its optimal decisions. The decision‐making problem becomes more difficult especially when the historical data is incomplete, or when the data is not available. This paper proposes an optimal decision‐making framework for a large consumer in the face of a comprehensive set of uncertain resources including electricity market price, internal electricity consumption, renewable power generation, and DR participation. To this end, a probabilistic‐possibilistic methodology is proposed to model the uncertainty of the electricity market prices in a more accurate way in comparison with existing approaches through the concept of Z‐number method. The uncertainty of internal electricity consumption is modelled by means of the seasonal ARIMA model, while the stochastic generation of its existing renewable resources are modelled through appropriate probability distribution functions. Moreover, the uncertainty of DRPs is considered by Markov chain. The large consumer also could sign bilateral contracts with generation companies to provide a portion of its required electricity demand. The simulation results confirm the effectiveness of the Z‐number methodology in comparison with other techniques for handling the uncertainties.

Optimal Spot Pricing Evaluation in Restructured Electrical Power System

Electricity markets in both developed and developing countries have been considerably reorganized over the previous two decades. The unbundling of generation and transmission results in restructured electricity market which enhances the competition among the market traders in the regime of open access. Therefore, the transmission pricing methods should be skilled in translating transmission costs into tariffs to allow participation, which leads to profitable effectiveness, allows the grid owner to recover prices, and makes market participants aware of the system's supply defense and consistency maintained. This research intends to (1) examine the drivers behind power transmission pricing, (2) Create AC-DC OPF-based Nodal Pricing methodology, and (3) work out prices for India’s real transmission network to enable market participants to compete and make the best decisions. Finally, the study indicated that the planned methodology is more appropriate for rising nations to achieve the goals of building extensive energy markets.

A hybrid robust-stochastic optimization approach for day-ahead scheduling of cascaded hydroelectric system in restructured electricity market

Uncertainties arising from complicated natural and market environments pose great challenges for the efficient operation of cascaded hydroelectric systems. To overcome these challenges, this paper studies the day-ahead scheduling of cascaded hydroelectric systems in a restructured electricity market with the presence of uncertainties in electricity price and natural water inflow. To properly model the uncertainty, we consider the unique characteristics of these two types of uncertainties and capture them via the uncertainty set and stochastic scenarios, respectively. A hybrid robust-stochastic optimization model is developed to simultaneously hedge against these two types of uncertainties, which is formulated as a large-scale non-convex optimization problem with mixed integer recourse. After introducing linearization of nonlinear terms, a tailored hybrid decomposition scheme combining Lagrangian relaxation and Dantzig-Wolfe decomposition is adopted to achieve efficient computation of the proposed model. Two real-world cases are conducted to demonstrate the capability and characteristics of the proposed model and algorithms.

Estimation of Locational Marginal Pricing Using Hybrid Optimization Algorithms

At present, the restructured electricity market has been a prominent research area and attracted attention. The motivation of the restructuring in the power system is to introduce the competition at various levels and to generate a correct economic signal to reduce the generation cost. As a result, it is required to have an effective price scheme to deliver useful information about the power. The pricing mechanism is dependent on the demand at the load level, the generator bids, and the limits of the transmission network. To address the congestion charges, Locational Marginal Pricing (LMP) is utilized in restructured electricity markets. To improve the system efficiency, in this paper, a Hybrid Backtracking Search Optimization Algorithm with Grey Wolf Optimization (GWO-HBOA) is proposed, based on the Security Constrained Economic Dispatch (SCED). The objective of the proposed scheme is to minimize fuel cost in the transmission system under the loss and lossless conditions, considering both the normal and congestion conditions. BOA is conducted first, and Grasshopper Optimization Algorithm (GOA) is subsequently combined with the BOA, which results in the GWO-HBOA. To predict the demand for generator power, the demand response is estimated exactly. Moreover, the load LMP can be segregated at service security levels with respect to various load entities. In this way, the overcharging and underpayment issues can be solved under the security constrained market optimization. Furthermore, to determine the LMP loss, DC optimal power flow is analyzed. The proposed GWO with constrained security is estimated on IEEE 30-bus test system. Compared with the existing techniques, the proposed algorithm achieves better performance, in terms of fuel cost, voltage stability, voltage deviations, real power loss, and reactive power loss.

Wind farm integrated restructured electricity market analysis using Ant Lion optimizer algorithm

ABSTRACT The optimal market strategy investigation is an enormous task for the system operator in a wind-integrated restructured power system. Bidding makes competition among all the market players in an electricity market and it forces to operate transmission lines near to their thermal limit and as an outcome, congestion may occur in the transmission system. The wind farm can take a vital role in this situation to mitigate transmission congestion by providing some additional powers to the system. To present the imprint of the wind farm in a deregulated power system, the work is formulated in a congested transmission environment considering the maximization of social welfare (SW) as an objective function. A generator shift factor (GSF)-based approach is presented in the work to mitigate the transmission congestion by rescheduling the supplier power. The power transfer distribution factor (PTDF) decides the optimal place of the wind farm (WF) in the system. Ant lion optimizer (ALO) algorithm is used in a modified IEEE 30-bus system to analyze the work. To validate the results obtained from the ALO algorithm, a comparison study is performed in this work with other three meta-heuristic algorithms namely particle swarm optimization (PSO), artificial bee colony (ABC), and moth swarm algorithm (MSA). The comparative results show that the ALO algorithm is much effective than the other three considered algorithms for the presented problem formulations. Four different real-time wind speed data are taken to check the efficiency of the presented approach with considering the intermittent nature of wind speed. It is revealed that the social benefit is raised with increment in wind speed irrespective of optimization techniques.

Price Freezes and Gas Sensitivity: An Estimation of the Price Impact of Electricity Market Restructuring

Price Freezes and Gas Sensitivity: An Estimation of the Price Impact of Electricity Market Restructuring

Competition in Power Generation: Ex-ante Analysis of Indonesia’s Electricity Market

This research optimises the mix and structure of Generation Companies (GenCos) in the Sumatra power system, Indonesia. Market power, indicating the ability to raise prices profitably above the competitive level, tends to be a significant problem in the aftermath of electricity market restructuring. In the process of regulatory reform and the development of competitive electricity markets, it is desirable and practical to establish an efficient number of competitor GenCos. Simulations of a power system account for multi-plant mergers of GenCos subject to a regulatory measure of the Residual Supply Index and the influence of direct current load flow and the topology of the system. This study simulates the Sumatra power system in order to determine the following: optimal market structure, efficient GenCo generation mix, and the optimal number of competitive GenCos. Further, this study seeks to empirically optimise the electricity generation mix and electricity market structure of the Sumatra power system using DC load flow optimisation, market power index, and multi-plant monopoly analysis. The simulations include generation and transmission constraints to represent network constraints. This research is the first to analyse the Sumatra power system using imperfect (Cournot) competition modelling. Furthermore, this study is the first kind to optimise the mix and structure of the Sumatra generation power market. The guidelines and methodology in this research can be implemented in other countries characterised by a monopoly electricity utility company.

Reliability and resilience in a regulated electricity market: Hong Kong under Typhoon Mangkhut

Hong Kong has resisted the global trend toward electricity market restructuring and deregulation, and maintains two vertically integrated utility monopolies. However, the regulated market achieves impressive performances with relatively low tariffs, rare electricity disruptions, and quick service restoration following outages. This research examines electricity reliability and resilience in the context of Typhoon Mangkhut in September 2018, with a particular focus on how the regulatory framework of the Scheme of Control Agreement (SCA) shapes planning, operating, and investing decisions. The SCA quantitively establishes financial incentives for utilities to achieve high-quality service and guard against potential political influence on utility decision-making.

Energy Storage Economic Analysis of Multi-Application Scenarios in an Electricity Market: A Case Study of China

Energy storage has attracted more and more attention for its advantages in ensuring system safety and improving renewable generation integration. In the context of China’s electricity market restructuring, the economic analysis, including the cost and benefit analysis, of the energy storage with multi-applications is urgent for the market policy design in China. This paper uses an income statement based on the energy storage cost–benefit model to analyze the economic benefits of energy storage under multi-application scenarios (capacity, energy, and frequency regulation markets) in China’s future electricity market. The results show that the economic benefits of energy storage can be improved by joining in the capacity market (if it exists in the future) and increasing participation in the frequency regulation market. Nevertheless, the benefits under multi-application scenarios can hardly guarantee the cost recovery of energy storage under the current market mechanism or at the current price levels. Moreover, the economic benefits under different subsidy policies are studied, and the results show that energy storage can recover the cost with appropriate subsidy policies (the subsidy of 0.071 USD/kWh for pumped storage power stations is sufficient while the subsidy of 0.142 USD/kWh is required for electrochemical power stations). Finally, the sensitivity analysis of an energy storage power station to different price levels is carried out considering the difference in electricity price between China and the United States.

Electricity Market Restructuring and Retail Rates

State-regulated investor-owned utilities serve over 70 percent of electricity to final end-use retail consumers in the U.S. Prior to the 1990s, all 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. From 1996-2000, some states passed electricity market “restructuring” that, over time, required utilities to separate the generation portion of their operations and allowed customers to purchase power from third party providers (commonly referred to as “retail choice”). This empirical research examines the effect of restructuring on electricity prices to final consumers. While these policies were generally advertised to reduce rates for final customers, in fact rates increased in restructured states relative to plausible counterfactual synthetic controls. We investigate plausible mechanisms for this finding; we find evidence that retail rates in restructured states are more responsive to natural gas price changes, the marginal fuel source during the period of analysis, and natural gas prices nationally increased in the post-restructuring era.

Solar Power Trading Models for Restructured Electricity Market in India

Solar Power Trading Models for Restructured Electricity Market in India

Electricity restructuring and the relationship between fuel costs and electricity prices for industrial and residential customers

A major concern with electricity market restructuring has been the possibility that fuel price volatility may leak into electricity prices. This view is credible if electricity price – fuel costs relation is stronger in restructured than in non-restructured states. We examine the validity of this view using panel data on for industrial, residential and commercial sectors in US states. The analysis accounts for cross-sectional dependency, cross-state heterogeneity, and interdependency of state fuel markets. The common correlated effects mean-group estimates suggest that (1) coal and natural gas costs Granger-cause electricity prices for industrial and commercial customers in both non-restructured and restructured states; (2) both fuel costs also Granger-cause electricity prices for residential customers in non-restructured states but only natural gas costs cause electricity prices in restructured states; (3) the long-run impacts of fuel costs on electricity prices are higher in non-restructured than restructured states; and (4) electricity prices exhibit lower persistence in non-restructured than in restructured states. These results are do not support the view of higher integration between input costs and electricity prices in restructured markets. Policymakers should be aware of the differences in electricity price response in alternative market structures when implementing new federal policies.

A comprehensive review on day‐ahead electricity market and important features of world's major electric power exchanges

Restructuring to the existing electric power system has transformed the conventional method in which electric power is traded around the globe. Now private players and entities having distributed generators can actively participate in restructured electricity market. It brings competition among different market players and leads to an overall fall in electricity prices. To further enhance competitiveness and bring transparency in the electric power trading mechanism, a common platform called power exchange (PX) has been established in many countries of the world. PX is a common internet-based online platform where various market players submit their offers/bids for selling/buying electrical energy or services. As in today's restructured electricity market, significant percentage of short-term electric power is traded through day-ahead market (DAM) of PX, the study of DAM is very important. Therefore, this article presents a comprehensive review of day-ahead electricity market and other important features of world's major electric power exchanges. It mainly focused on PX products, trading mechanism, DAM design issues, world's major PX, comparative analysis of DAM of world's major PX and future challenges.