Deregulated Electricity Research Articles

Forecasting Half-Hourly Electricity Prices Using a Mixed-Frequency Structural VAR Framework

Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this paper, we employ a mixed-frequency vector autoregression (MF-VAR) framework where we propose a VAR specification to the reverse unrestricted mixed-data sampling (RU-MIDAS) model, called RU-MIDAS-VAR, to provide point forecasts of half-hourly electricity prices using several weather variables and electricity demand. A key focus of this study is the use of variational Bayes as an estimation technique and its comparison with other well-known Bayesian estimation methods. We separate forecasts for peak and off-peak periods in a day since we are primarily concerned with forecasts for peak periods. Our forecasts, which include peak and off-peak data, show that weather variables and demand as regressors can replicate some key characteristics of electricity prices. We also find the MF-VAR and RU-MIDAS-VAR models achieve similar forecast results. Using the LASSO, adaptive LASSO, and random subspace regression as dimension-reduction and variable selection methods helps to improve forecasts where random subspace methods perform well for large parameter sets while the LASSO significantly improves our forecasting results in all scenarios.

Efficiency and consumer welfare under retail electricity deregulation: Analysis of Ohio's retail choice markets

AbstractMany critical infrastructure services operate under either price regulation or deregulated systems. The grand policy experiment of deregulation has been heavily studied, but evaluations have been limited at the retail level where end‐user prices are often difficult to obtain. This paper presents an in‐depth look at the retail market for electricity in a retail deregulated, or restructured, market—Ohio. We build and introduce a comprehensive SQL database of every daily filed retail electricity offer over a 9‐year period of study, over two‐million records. We integrate this data with other external data sources and conduct a detailed descriptive analysis of market prices as observed by end consumers at the retail level. We find that the lion's share of “competitive” retail electricity choice offers are more costly to consumers than the utility's default service rate and have higher markups over the wholesale price, and we find that when prices exceed the default rate they do so by a considerably larger margin than when consumers observe savings. We also find that even well‐informed consumers are able to find a welfare‐improving rate relative to the default rate between only 43% and 59% of the time. We conclude with a discussion of implications and root economic causes of the efficiency and consumer welfare problems we observe.

Sensitivity Analysis of a Nuclear Hybrid Energy System With Thermal Energy Storage in Deregulated Electricity Markets Considering Time Series Uncertainty in Electricity Price

ABSTRACTAdding thermal energy storage to nuclear power plants has been proposed as a way to allow nuclear plants to operate more flexibly and potentially be more competitive in deregulated electricity markets. The economics of these systems in deregulated markets are subject to uncertainties in capital costs, operating costs, and revenue. This study quantifies the uncertainty in the net present value of a nuclear power plant with integrated thermal energy storage in three U.S. deregulated electricity markets considering these sources of uncertainty and quantifies, for the first time, the relative contributions each source makes to the overall uncertainty. To accomplish this, a computationally efficient block bootstrap method is introduced to quantify uncertainty contributions from the stochastic time series of electricity prices, achieving a two order of magnitude decrease computational time compared to the model‐based methods used in previous works while also relaxing several strict assumptions made by the model‐based approach. Up to 18.5% of the overall variance in net present value is attributable to variance in the electricity price stochastic process, with this sensitivity varying significantly across markets.

Iterative LMI-Tuned PID Controller for Robust Decentralized Multi-Area Automatic Generation Control in Deregulated Electricity Markets

This study proposes an Iterative Linear Matrix Inequality (LMI)-Tuned Proportional-Integral-Derivative (PID) controller for robust decentralized AGC in the context of deregulated electricity markets. The study addresses the challenges posed by market uncertainties and diverse generation sources, aiming to enhance the stability and performance of power systems. This study is driven by the urgent need to develop robust AGC strategies that are adapted to the complex dynamics of decentralized power systems operating in deregulated markets. The conventional centralized control approaches are often inadequate to address the diverse and distributed nature of modern generation units. Furthermore, the intermittent and variable nature of renewable energy sources adds a layer of complexity that demands innovative control solutions. The impact of bilateral contracts on the dynamics is taken into consideration while modifying the conventional LFC of interconnected power networks. To model these bilateral contracts, the distribution company (DISCO) participation matrix (DPM) is presented, and the multi-area block diagram reflects these concepts. Simulation studies are conducted to evaluate and compare the performance of the proposed Iterative LMI-Tuned PID Controller and Conventional PID Controller. Results are analyzed against key performance metrics, considering scenarios reflective of diverse market conditions. Comparative studies with the conventional PID controller provide insights into the efficacy and robustness of the proposed approach.

A Review on Techniques of Power Transmission Lines Congestion Management in Deregulated Electricity Market

The idea of market power has gained its significance after the electric power industry started a practice of transition and restructuring since early Nineteen ninety. In this competitive electricity market, the physical and operational the constraints of the network hold key intimidation to the market by the generation companies. The growth of deregulated power systems has an outcome in terms of overloading transmission networks or network congestion. Congestion has severe effects on power systems, which includes rigorous system damage. Congestion take place when transmission systems fail to deliver power based on the load demand. These problems can be managed by implementing congestion management methods, which has a significant role in current deregulated power systems. Congestion has created the risk to reliability and power system security through defiance of transmission ability limits of line. This paper reviews some of important techniques and meticulous used by various researcher for management of congestion of lines. The exertion of various publications is used to review the implication of each anticipated technique in relieving congestion and optimizing system operating costs.

Energy Management Using PTDF in a Deregulated Electricity System

Energy Management Using PTDF in a Deregulated Electricity System

A Privacy-Preserving State Estimation Scheme for Smart Grids

With the appearance of electric energy market deregulation, there exists a growing concern over the potential privacy leakage of commercial data among competing power companies where data sharing is essential in the applications such as smart grid state estimation. Most of the existing solutions are either perturbation-based or conventional cryptography-based where a trusted central 3rd party would often be required. This paper proposes privacy-preserving state estimation protocols for DC and AC models. The proposed idea is to distribute the overall task of the system state estimation into sub-tasks which can be performed by local sub-grid operators with their private data. A masking method is designed inside a homomorphic encryption scheme which is then used to ensure both the input and output data privacy during the collaboration process among individual sub-task players. Security is achieved via the computationally indistinguishable post-quantum security guaranteed by a levelled homomorphic encryption scheme over real numbers and the differential privacy of the output estimated states provided by the Laplace mechanism perturbation integrated into the masking linear transformation. Simulation results are presented to demonstrate the validity of our proposed privacy-preserving system state estimation protocols.

Optimal Congestion Management with FACTS Devices for Optimal Power Dispatch in the Deregulated Electricity Market

A deregulated electricity market provides open access to all market players. In an open-access power market, the system operator is responsible for ensuring that all contracted power is dispatched. However, if this results in line flows that exceed their acceptable range, then it could threaten the system’s security. Therefore, the system operator checks for congestion as the line flow exceeds its limit. For congestion management, the system operator applies different curtailment strategies to limit the requested transaction. Therefore, in this work, an optimal power dispatch model has been presented in order to reduce the curtailment of requested power. A modified moth flame optimization technique has been implemented to frame this OPD model. The impact of congestion management on power dispatch has been analyzed, considering bilateral and multilateral dispatch in an electricity market. In addition, the effect of FACTS devices on reducing congestion and curtailing power is studied. Verification studies showed that the proposed solution reduces congestion costs by 27.14% and 29.4% in 14- and 30-bus systems, respectively. It has been verified that the MMFO approach with the FACTS device improves transaction deviations and ensures that the deregulated system provides secure energy with less cost reflected on the customers.

Electricity industry (de)regulation and innovation in negative-emission technologies: How do market liberalization influences climate change mitigation?

Deregulation is widely considered a driver of competition and reduction of market inefficiency and can lead to innovation. We deepen the literature and policy understanding of this narrative by extending it to negative emission technologies while we strengthen the findings of previous studies by accounting for input research and development (R&D) expenditure with heterogeneity analyses. The authors adopt the staggered difference-in-difference (SDID) design to 25 OECD countries for the period 1985–2015 to examine the influence of deregulation on innovation in the electricity industry with an emphasis on negative emission technologies. The empirical results show that while electricity deregulation is generally associated with increased innovation, only intense market deregulation can promote innovation in carbon capture and storage (CCS). This benefit is more observed in European Union OECD countries and those with high output innovation performance. The results also show that when implemented after market deregulation, solar PV and Wind power Feed-in tariffs effectively promote renewable energy innovation. The results suggest that electricity deregulation is a compelling market tool to promote climate change mitigation since the competitive market it creates supports the advancement of CCS and renewable energy innovation. We recommend a deregulation process to accommodate an effective market model designed to incentivize the promotion of funding for negative emission technologies.

Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid

The deregulation of electricity markets has driven the need to optimise market bidding strategies, e.g. when and how much electricity to buy or sell, in order to gain an economic advantage in a competitive market environment. The present work aims to determine optimal day-ahead market bidding curves for a microgrid comprised of a battery, power generator, photovoltaic (PV) system and an electricity load from a commercial building. Existing day-ahead market bidding models heuristically fix price values for each allowed bidding curve point prior to the optimisation problem or relax limitations set by market rules on the number of price–quantity points per curve. In contrast, this work integrates the optimal selection of prices for the construction of day-ahead market bidding curves into the optimisation of the energy system schedule; aiming to further enhance the bidding curve accuracy while remaining feasible under present market rules. The examined optimisation problem is formulated as a mixed integer linear programming (MILP) model, embedded in a two-stage stochastic programming approach. Uncertainty is considered in the electricity price and the PV power. First stage decisions are day-ahead market bidding curves, while the overall objective is to minimise the expected operational cost of the microgrid. The bidding strategy derived is then examined through Monte Carlo simulations by comparing it against a deterministic approach and two alternative stochastic bidding approaches from literature.

Earning While Learning: An Adversarial Multi-Armed Bandit Based Real-Time Bidding Scheme in Deregulated Electricity Market

As the specific incarnation of cyber-physical-social systems, in deregulated electricity market, the market gaming behaviors may have significantly affected the costs of electricity delivered to the market. Especially, from the supply side, the primary goal of power generating companies (PGCs) is to develop strategic biddings to maximize their profits in long-term trading, when facing intrinsic uncertainty. Typically, in such repeated and dynamic settings, one fundamental challenge is that, any PGC neither has prior knowledge about all unknown opponents’ incentives, nor observes their strategies and obtained profits. Especially, the common setting is that, once the bidding auction has occurred, the PGC only observes the market clearing price (MCP) at each round, and winning or losing status. While it is typical to assume some perfect or bounded rationality model of the PGCs, their real behaviors do not follow such assumptions due to lack of complete information, computational intractability, or lack of perfect execution, etc. We formulate the problem of sequentially optimizing any PGC's bids with an adversarial multi-armed bandit (MAB) model. Specifically, at each round, a PGC chooses to play against all other opponents from an infinite set of possible strategies that are split into continuous intervals by sequentially occurred MCPs. Then at the end of each round, the PGC observes the outcome of the auction and updates its estimation on the expected bid's fitness for each interval (i.e., how much the expected profit of the interval could be achieved), and selects the bid for the next round using the proposed algorithm Exp3C (i.e., exponential-weight for exploration and exploitation with continuous value). The experimental results based on real dataset demonstrate that Exp3C performs better than other heuristic schemes including pure greedy, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\varepsilon}}$</tex-math></inline-formula> -greedy and MCP predication based bidding schemes. Moreover, we theoretically prove the upper bound of average Exp3C regret per round follows <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{O}} ({2/\sqrt {\boldsymbol{T}} })$</tex-math></inline-formula> , where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> is the number of total rounds. In summary, the proposed Exp3C has two distinguished advantages. First it is distributed, since its decisions uniquely depend on its past decisions and profits. Second, it is rational, since a PGC is given guarantees on its own accumulated profit regardless of other PGCs’ behaviors.

A Cooperative Energy Transaction Model for VPP Integrated Renewable Energy Hubs in Deregulated Electricity Markets

This article proposes a cooperative energy transaction model for a virtual power plant (VPP) developed as a bilevel optimization program based on the Stackelberg game in the deregulated electricity markets. The VPP integrates multiple renewable energy hubs consisting of electric vehicle charging stations with photovoltaic and battery energy storage systems. The proposed model derives co-optimized strategic decisions for the VPP operator in the day-ahead (joint active power, reserve, and reactive power) markets while considering the balancing market operation. The proposed model is reformulated as a mathematical program with equilibrium constraints (MPECs) using the Karush–Kuhn–Tucker conditions and the strong duality theorem. For an efficient solution, the MPEC is approximated as a mixed-integer linear program by applying the Fortuny-Amat transform on the complementarity and slackness conditions and the binary expansion on the bilinear terms. The uncertainties of the renewables, loads, and prices are modeled using stochastic scenarios. Finally, comparative case studies of a VPP in an IEEE 33 node active distribution network show that the proposed model improves the profit by strategic cooperative energy transactions.

Design Optimization Method and Performance Analysis of 90MW Grid-Connected Solar PV system in Cambodia

Large-scale PV plants have the ability to make the curtailment of cost per watt of the normal power installed. This article describes a new technique for computing the optimal contour of large PV plants. Massive infiltration of renewable energy sources is increasing because of the awareness created on environmental issues and electric power deregulation. A PV system is a renewable energy source that establishes a future sustainability in power distribution. The technical specifications details are provided by solar irradiation and performance ratio. High accuracy of energy generation followed in computations in the proposed system. The high-accuracy feature is validated using experimental data of the 90MW PV plant in Cambodia. The results obtained show that the optimisation method can lead to a reduced cost of energy generated. This enables the maximisation of the benefit obtained on the operational life period of the PV module. The model’s accuracy depends on a hierarchical structure level model, model measurement accuracy, model identification, structure accuracy, measurement accuracy, simulator algorithms metrics, and the accuracy of the calibration parameter. The paper is built based on the physical ranking of the PV power model structure, making the system a high-fidelity model.

Flexible Forward Contracts for Renewable Energy Generators in Deregulated Electricity Markets

Flexible Forward Contracts for Renewable Energy Generators in Deregulated Electricity Markets

Analysis of Hourly Price Granularity Implementation in the Brazilian Deregulated Electricity Contracting Environment

Analysis of Hourly Price Granularity Implementation in the Brazilian Deregulated Electricity Contracting Environment

A Similar Day Based Short Term Load Forecasting Method Using Wavelet Transform and LSTM

With the deregulation of electricity markets, short‐term load forecasting (STLF) has gained importance for the operation of power systems. However, an effective STLF model is hard to achieve as the load is affected by various factors. Here we present a STLF method based on similar day approach to predict the electricity usage 24 h ahead and by employing long short‐term memory (LSTM) and wavelet transform to further improve the forecasting accuracy. Compared with other methods, the proposed method achieves higher accuracy, and brings out the significance of using similar day's load, wavelet transform, and LSTM network. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Deregulated electricity market, a stochastic variational approach

This paper focuses on the study of an electricity market model, which evolves in T+1 stages, so that, at each stage, a continuum state of nature is possible. The decision-making framework of large consumers is considered: agents consume, they have the opportunity to produce, and the capability of signing contracts. Uncertainty on future possible stages leads to the problem being located in a filtered probability space. The aim is to reformulate the problem as a stochastic quasi-variational inequality, in order to obtain an existence result of equilibrium solutions.

Robust calculation method of voltage stability margin based on voltage‐collapse‐point properties

The progress in electricity deregulation has made electricity systems more economical while maintaining stability. Therefore, the stability of an electricity system should be evaluated more strictly than done previously. We propose a robust calculation method of voltage stability margin (VSM). The proposed method is based on the optimal‐power‐flow‐based direct method (OPF‐DM), which can calculate VSM directly when complementarity constraints related to limits for reactive‐power generation are added. However, the additional complementarity constraints may cause a convergence and local solution issues because of the numerical complexity. The proposed method calculates the appropriate VSM on the basis of several voltage‐stability properties. We implemented the proposed method on MATPOWER and evaluated it with IEEE 9‐bus, 30‐bus system, 39‐bus, 300‐bus, PEGASE 1354‐bus, and IEEJ WEST10‐O/V systems. The results indicate that the proposed method can calculate VSM robustly compared with conventional methods. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

The states that opted out: Politics, power, and exceptionalism in the quest for electricity deregulation in the United States South

This article addresses why southern U.S. states opted against joining regional electricity markets at the end of the 20th century by examining the history of deregulation in North Carolina, South Carolina, Georgia, and Florida. Although there has been considerable attention paid to outcomes in states that embraced a market model for electricity, the one-third of the country (by population) that did not has been largely ignored. Yet in 2021, states that rejected electricity markets are home to the utility serving the greatest number of customers (Duke Energy) and the utility with the greatest market value (NextEra Energy), giving the region outsized impact on the U.S. electricity system. In this article we draw on archival research and in-depth interviews with stakeholders that took part in the debate about joining markets. After using archival sources to recount the events that led to the rejection of deregulation, we utilize our interviews to illustrate three key trends: (1) how the battle lines get drawn between utilities, industrial customers, legislators, and regulators; (2) the methods used and extent to which utilities opposed deregulation in their states; and (3) the role of southern exceptionalism in shaping the deregulation debate. As southern states question anew whether a market might improve regional energy outcomes, understanding why these states originally rejected markets proves illuminating.

Re-dispatch and Application of FACTs for Transactions Allocation in Deregulated Electricity Markets

In the current scenario, transmission cost allocation is one of the significant difficulties arises due to the expansion in power exchanges in transmission open access looked by electric energy area. The proposed method calculates transmission cost allocation by considering bilateral &amp; multilateral transactions. The transmission cost allocation approach in the proposed method derives from equivalent bilateral which states that a small amount of every generator provides with each request in consistently and separated manner. The influence of power flow in network via all lines due to a transaction is measured by power flow solution. This paper discusses the congestion problem in the deregulated electricity market using an optimal power flow (OPF). The transmission lines are uncongested using re-dispatch method and then taxes are calculated for the establishment of the flexible A.C. transmission systems (FACTS) devices in the transmission network to reduce taxes. The excellent location of FACTS device can be identified by the bus on which highest T. The result indicates that the combination of TCSC and SVC incredibly discount the measure of re-dispatched power which provides optimal operating point nearer to the market settlement. Hence, TCSC and SVC gives convenient option to block the execution of transmission estimation utilizing approaches and calculate the transmission price.