Electricity Market Operation Research Articles

The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand

This paper presents an interactive trading decision between an electricity market operator, generation companies (GenCos), and the aggregators having demand response (DR) capable loads. Decisions are made hierarchically. At the upper-level, an electricity market operator (EMO) aims to minimise generation supply cost considering a DR transaction cost, which is essentially the cost of load curtailment. A DR exchange operator aims to minimise this transaction cost upon receiving the DR offer from the multiple aggregators at the lower level. The solution at this level determines the optimal DR amount and the load curtailment price. The DR considers the end-user’s willingness to reduce demand. Lagrangian duality theory is used to solve the bi-level optimisation. The usefulness of the proposed market model is demonstrated on interconnection of the Pennsylvania-New Jersey-Maryland (PJM) 5-Bus benchmark power system model under several plausible cases. It is found that the peak electricity price and grid-wise operation expenses under this DR trading scheme are reduced.

A power system scheduling model with carbon intensity and ramping capacity constraints

The integration of European electricity markets aims at market coupling among interconnected power systems and the evolution of environmentally friendly technologies. This process is anticipated to utilize more efficiently the flexible generation and interconnections transmission capacity and provide environmental and economic benefits to final consumers. This paper presents a mixed integer linear programming model for the optimal scheduling of a power system (unit commitment problem) simulating the day-ahead electricity market. The model determines the optimal daily power generation mix, the electricity trade with neighboring countries, the evolution of the system's marginal price and the resulting environmental impact. The model incorporates CO2 emissions intensity constraints and introduces flexible ramping products, in addition to reserve requirements, aiming to identify their impacts on both operational and economic decisions. The model is applied on the Greek power system and its interconnections with neighboring power systems in Southeast Europe. The proposed approach can provide useful insights on the optimal generation and interconnections portfolio that meets the real electricity market operating needs of contemporary power systems with environmental and ramping capacity constraints.

A novel pricing scheme for managing virtual energy communities and promoting behavioral change towards energy efficiency

The harmonization between the variable rate of energy production in the era of massive renewable energy penetration is a major challenge in an open, competitive and resilient electricity market. As a result, there is an increasing need for modern pricing schemes, which will effectively incentivize willing users to modify their energy consumption pattern to meet this objective. Current energy pricing schemes (e.g. real time pricing) treat all users the same, and do not adequately compensate for behavioral changes, thus mitigating the behavioral change dynamics. In this paper, we propose a Community Real Time Pricing (CRTP) scheme together with an Energy Community Formation Algorithm (ECFA), where users are clustered in Virtual Energy Communities (VECs) according to: (i) their level of flexibility in modifying their Energy Consumption Curve (ECC), and (ii) their relationships in Online Social Networks (OSNs), modelling peer-pressure capabilities. We show that CRTP with ECFA can simultaneously achieve considerable reduction in the system’s energy cost and greater aggregated users’ welfare than with the state-of-the-art real time pricing. CRTP–ECFA adopts a truly fair pricing policy, as each user is rewarded exactly according to his/her individual contribution in reducing system costs, thus promoting the desired behavioral change.

Influence of Wind Power on Intraday Electricity Spot Market: A Comparative Study Based on Real Data

Overtime, in the electricity sector, there has been a technological transfer to renewable electricity generation. With this change, processes, in the economic and availability terms, are expected to improve. In this new paradigm, society demands electricity without an impact on the environment and with the lowest possible cost. The wind power (WP) integration appears in this evolution process by achieving important technological advances, supporting in 2017 a growth of 44% of new projects in Europe, higher than any other renewable technology. However, the renewable energy sources (RES) integration in the electricity networks still presents technical difficulties and challenges, leading to challenges in the electricity markets (EMs). Therefore, this work evaluates the importance of WP and its influence on the Iberian Electricity Market (MIBEL), at the level of the intraday electricity spot market (IESM). This is an innovative study because literature usually focuses on day-ahead WP impact and this study focuses on intraday markets, which are closer to the consumption periods. The goal was to make an analysis on the impacts when betting on WP sources, in order to improve the market interaction with WP integration, considering as criteria the consumer satisfaction, in terms of lower electricity prices and WP availability. For this study, the market bids registered by the Iberian Electricity Market Operator (OMIE), from 2015 to 2017, ran over a new market simulator, specially developed for this proposal, considering a virtual market condition, but not considering the bids made by WP producers. The comparison of the results allowed the evaluation of the WP influence on the EM quantitative, which is noteworthy.

The Quiet Undoing: How Regional Electricity Market Reforms Threaten State Clean Energy Goals

In a series of largely unnoticed but extremely consequential moves, two regional electricity market operators are pursuing reforms to make it more difficult for states to achieve their clean energy goals. The federal energy regulator, FERC, has already approved one such reform and ordered a second market operator to go farther in punishing state-supported clean energy resources than it had initially proposed. This disturbing trend highlights a shift in energy governance that threatens to destabilize the field’s delicate cooperative federalist model. Over the past several decades, states have increasingly ceded control over energy dispatch and grid planning to private market operators, on the understanding that these neutral entities would design regional markets to accommodate state resource preferences. Now, several operators are alleging that state clean energy policies “harm” their markets — with scant support as to why — and have instituted reforms intended to protect fossil-fuel resources from the impacts of state policy preferences. In this essay, we bring to light the ways in which the intricate, technical reforms underway in regional electricity markets threaten state climate change objectives and the durability of FERC’s regional market constructs. If FERC allows private market operators to impose their policy preferences on participating states — or if FERC requires pro-fossil market designs — progress in decarbonizing the electricity sector will likely slow. At the same time, the potential for greater regional cooperation in electricity markets — a critical strategy for integrating a high penetration of renewable energy onto the electricity grid — will diminish.

Coordination of wind generation and demand response to minimise operation cost in day‐ahead electricity markets using bi‐level optimisation framework

Demand response (DR) can play an important role when dealing with the increasing variability of renewables in power system. This study proposes a bi-level market model for wind-integrated electricity market, where the DR requirement is paired with the wind profile to deal with wind variability. At the upper level, an electricity market operator aims to minimise the day-ahead operation cost considering plausible wind generation scenarios. At the lower level, the DR exchange operator aims to maximise social welfare by trading aggregated DR among several aggregators. The solution at this level determines the optimal DR amount and price setting for each aggregator. The DR from the flexible loads is modelled from the end-users' perspective considering their willingness parameter. The market model is formulated as a bi-level optimisation problem using Lagrangian relaxation with Karush–Kuhn–Tucker optimality conditions. The effectiveness of the proposed scheme is demonstrated on sample 4-bus and IEEE 24-bus systems. Different scenarios such as high, medium and low levels of wind and DR are investigated. The high-wind low-DR scenario leads to minimum operation cost and is least inconvenient for end users as it sets the DR at a minimum level while keeping the higher levels of wind generation.

An efficient edge sparse coding approach to ultra‐short‐term household electricity demand estimation

With the opening of electricity market, the interaction between grids and users is becoming more and more frequent. Household electricity demand estimation is a significant and indispensable process of the necessary precise demand response in the future. Large‐scale coverage of the Advanced Metering Infrastructure provides a large volume of user electricity data and brings opportunities for residential electricity consumption forecasting, but, on the other hand, it has brought tremendous pressure on the communication link and data computing center. This paper proposes an efficient edge sparse coding method based on the K‐singular value decomposition (K‐SVD) algorithm to extract hidden usage behavior patterns (UBPs) from load datasets and reduce the cost of communication, storage, and computation. The load of representative household appliances is introduced as the initial dictionary of the K‐SVD algorithm in order to make the UBPs more proximate to the residents' daily electricity consumption. Then, a linear support vector machine (SVM)‐based method with UBPs is used to predict the subsequent interval household electricity demand. The experimental result shows that the proposed algorithm can effectively follow the trend of the real load curve and realize accurate forecasting of the peak electricity demand. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion.

In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model.

Harnessing business intelligence in smart grids: A case of the electricity market

This paper discusses analytical aspects of smart grids and offers insights into the development of a business intelligence solution for the electricity market. The goal is to design a system that provides an emerging electricity market with the necessary data flows and information for forecasting, data analysis and decision making, leading to better business results and more control over the market. By employing a methodology specifically suited to the electricity market domain, we designed a business intelligence solution for the Serbian electricity market operator “Elektromreža Srbije”. The research results show that the proposed approach leads to more effective market management in data-rich smart grid environments, while still being dynamic enough to adapt to frequent rule changes in the still developing grids and their markets.

Design of robust profitable false data injection attacks in multi‐settlement electricity markets

The potential impacts of data integrity attacks on multi-settlement electricity markets have been recently investigated and have sent a strong message to power grids independent system operators (ISOs) that adversaries could launch profitable cyber attacks by casting an incorrect image of transmission lines congestion pattern. However, these cautionary messages may be underestimated due to the adversaries unrealistic requirements (e.g. having access to real-time measurements) to launch a successful stealthy and profitable attack. This study examines the potential of the aforementioned risk by demonstrating how a malicious power market participant could disturb the electricity market operation, using a pre-designed false data injection attack along with bogus electricity trades in both day-ahead and real-time markets. The proposed attack design is robust against market uncertainties and the adversary can guarantee the success of the attack in advance. Hence, the existence of such cyber attacks against electricity markets can make the adversaries more aggressive. The numerical results on the IEEE 14-bus test system confirm the vulnerability of multi-settlement electricity markets to such financial cyber attacks. The results obtained from investigating such an attack design can be employed by ISOs in order to provide appropriate countermeasures.

Wind Power Development and Energy Storage under China’s Electricity Market Reform—A Case Study of Fujian Province

This paper, based on the Fujian provincial 500 kV grid and part of the 220 kV grid and the key power plants, including hydro, coal, nuclear, gas, wind and pumping and storage hydro powers (PSHP) connected to the grid, constructs an independent electricity market model. Using data that are very close to reality about coal fired power production costs, along with data about power plants’ technical constraints, this paper studies the effect of wind power on Fujian’s provincial electricity market. Firstly, the paper analyzes the relationship between wind speed and wind power output and the effects of short-term power output fluctuation on frequency modulation and voltage regulation. Secondly, under supposition of the production costs following quadratic functions, the paper analyzes the effects of changes in wind power output on the electricity supply costs under optimal power flow. Thirdly, using the bidding model in the Australian Electricity Market Operator for reference and supposing that, in a competitive market, coal fired power plants can bid 6 price bands according to their capacity, the paper analyzes effects of wind power on electricity prices under optimal power flow, the stabilizing effects of PSHP and the minimum PSHP capacity needed to stabilize the electricity market. Finally, using a daily load curve, this paper simulates the electricity prices’ fluctuation under optimal power flow and PSHP’s stabilizing effect. The results show that, although PSHP has a large external social welfare effect, it can hardly make a profit. In the end, this paper puts forward some policy suggestions for Fujian province’s wind and nuclear power development, PSHP construction and electricity market development.

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then, we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a two-settlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.

An Enhanced Network-Constrained UC Model for Leveraging System Operation Cost and Financial Profitability of Incentive-Based DR Loads

Demand response (DR) programs provide consumers opportunities to play an active role in electricity market operation by encouraging them to reduce and/or shift their electricity usage during peak periods and/or when system security is jeopardized. There are primarily two types of DR programs, including incentive-based DR and price-based DR. This paper focuses on the incentive-based DR. For this type of DR program, although shifting peak loads to off-peak periods could reduce system operation cost, it may raise off-peak electricity prices and the incentives may not be enough to cover the increased payments of DR loads. This would diminish consumers' enthusiasm to the continued participation in DR programs. Federal Energy Regulatory Commission Order 745 states that when dispatching a DR resource as an alternative to a generation resource is cost-effective, which is determined by the Independent System Operator's (ISO) net benefits test, the DR must be compensated at full locational marginal price (LMP) value. However, the order focuses more on load curtailment, but not load shifting. In addition, the net benefits test mainly focuses on consumers' savings, while ignoring operation costs and the value lost by consumers, which may cause system inefficiency. This paper proposes a systematic model for leveraging system operation cost and consumer payment concerns in electricity market operation. The proposed model minimizes the system operation cost, while incorporating explicit LMP formulations and consumer payment requirements into the network-constrained unit commitment formulation. Thus, it determines the proper amount of DR loads to be shifted from peak hours to off-peaks under the ISO direct load control for reducing the operation cost and ensuring that DR load payments will not deteriorate significantly after load shifting. The proposed model is solved in its original mixed-integer nonlinear programming formulation and the mixed-integer linear programming reformulation via piece-wise linear approximation. Numerical case studies illustrate the effectiveness of the proposed model and compare the computational performance of the two formulations.

Impact of distributed generation on nodal prices in hybrid electricity market

Wind power integration in electrical utilities all over the world is increasing manifold due to environmental degradation concerns, technological innovations in turbine technology, smart grid initiatives, and competitive electricity market operation. In this paper, impact of distributed generation is presented on fuel cost and nodal prices in mix of pool and bilateral electricity market. In this paper, the main contributions are: (i) Mixed integer nonlinear programming (MINLP) approach for determining optimal location and number of distributed generators (ii) impact of wind power output on nodal prices of real and reactive power with wind power variation during 24 hours. The proposed MINLP based optimization approach has been applied for IEEE 24 bus reliability test system.

하절기 평일의 인공신경망을 이용한 24시간 전력수요 예측 알고리즘

The short-term load forecasting is essential for efficient operation of electricity market, economical operation plan of generators, and prevention of outage events. 24-hour load forecasting algorithm using neural network algorithm is proposed. The input parameters of the artificial neural network are composed of time index, hourly load data, hourly temperature data for the day before the forecasting day and hourly load data, hourly temperature data for two days before the forecasting day. The output parameters are hourly load data on the forecasting day. The artificial neural network training is performed using the past 28 training cases. The min-max normalization is used as a normalization method of input parameters such as time index, hourly load data, and hourly temperature data during training. The case studies show that the average percentage errors of the proposed algorithm are improved comparing with errors of the exponential smoothing method. The proposed algorithm is expected to contribute to the efficient operation of power system and electric power market by providing more accurate predictive load value of day ahead electricity demand.

Optimal automatic generation control of two-area power systems with energy storage units under deregulated environment

An attempt is made in this paper to present the application, design, and performance analysis of a novel optimal controller (OC) for automatic generation control (AGC) of interconnected two-area electrical power systems in a deregulated power environment with energy storage units. The OC is designed via full state vector feedback strategy to carry out the study. Swift acting energy storage units such as redox flow batteries (RFBs) are integrated into the power system models, and their efficacy in boosting AGC performance is executed and compared. Initially, the efficacy of OC is investigated in a restructured two-area single-source thermal system, and then, the study is extended to a proposed more realistic restructured two-area multi-source thermal-hydro-diesel-gas power system. It is observed that OC is able to satisfy the AGC requirement under different power contracts in the open electricity market and shows better performance in comparison to a recently published artificial cooperative search algorithm optimized proportional integral controller. MATLAB simulation results demonstrate the improvements in the dynamic performance of the system in the presence of RFB. Sensitivity analysis reveals the robustness of OC under ample variations in the system parameters, initial loading and size and positions of uncontracted load power demands.

Hierarchical aggregation method for a scalable implementation of demand side management

Demand side management (DSM) aims to efficiently manage power flow by engaging energy customers, through offering incentives via price signals to alter their consumption patterns or directly controlling their loads. However, the integration of renewable energy generators and batteries in residential premises requires new approaches for DSM as they offer more flexibility. Moreover, as there are often a large number of residential energy customers within a distribution network, it is quite challenging to accommodate all of them in the DSM. In this paper, we propose an Aggregated Method (AM) that allows the treatment of distribution grid as a composition of several microgrids, which helps to aggregate underlying energy customers’ power and energy constraints and operating preferences. In addition, we provide methods for distributing the aggregated energy demand decisions among the participating energy customers. In contrast to the alternative centralized method, our approach requires less computational time to obtain decisions and hence scales well with increasing network size. Moreover, our results indicate that when using our method, energy customers receive more benefits through satisfying their energy requirements and operating conditions. Our overall analyses showed that the proposed framework can be easily adopted by the electricity market operators to create scalable DSM programs.

Forecasting quantiles of day-ahead electricity load

Accurate load forecasting plays a crucial role in the decision making process of many market participants, but probably is most important for the dispatch planning of an electricity market operator. Despite the competitive forecast accuracy achieved by existing point forecast models, point forecasts can only provide limited information relating to the expected level of future load. To account for the uncertainty of future load, and provide a more complete picture of the future load conditions for dispatch planning purposes, quantile forecasts can be useful. This paper proposes a computationally efficient approach to forecasting the quantiles of electricity load, which is then applied to forecasting in the National Electricity Market of Australia. The proposed model performs competitively in comparison with one industry standard and two recently proposed quantile forecasting methods. One of the main advantages of the proposed approach is the ease with the number of covariates can be expanded. This is a particularly important feature in the context of load forecasting where large numbers of important drivers are usually necessary to provide accurate load forecasts.

Research on electricity market operation mechanism and its benefit of demand side participation

Demand response plays an important role in maintaining the economic stability of the system, and has the characteristics of high efficiency, low cost, fast response, good environmental benefits and so on. Demand side resource is an important part of electricity market. The research of demand side resources in our country is still in the initial stage, but the opening of the electricity sales side provides a broad prospect for the development of electricity market. This paper summarizes the main types of demand side resources in our country, analyzes the economic principle of demand response from the micro perspective, puts forward some suggestions on the operation mechanism of China’s demand side resources participating in the electricity market under the condition of electricity sales side opening, analyzes the current situation of pricing in the electricity wholesale market and sets up the pricing strategy of the centralized wholesale market with the demand side power supply participating in quotation, which makes the social and economic benefits reach the maximum.

Co-evolution entropy as a new index to explore power system transition: A case study of China's electricity domain

Power systems are bound to experience a long transition process, which involves not only changes in technology but also changes in user practices, regulations, industrial networks, infrastructures, and cultures. There are conflicts among these factors on account of the mutual uncoordinated changes, which would hinder the development of power systems. Therefore, keeping permanent coordination of numerous factors is a great challenge for the power system evolution. Current evaluation methods of power system evolution do not pay sufficient attention to the co-evolution of factors, particularly for technology, society, finance and policy, as well as insight from innovation studies and the sociology of technology. Hence, we propose a new index: co-evolution entropy, which can indicate the positive-negative entropy interaction coordinating system evolution. Then, based on socio-technical system, we identify 14 factors participating in the evolution of power system from three layers: landscape, regime and niche. Finally, we apply this index to China's electricity domain for identifying the evolution obstacles of non-coordinates, and propose a strategy to improve the system's co-evolution. Results show that (1) the change in co-evolution entropy from different layers has different characteristics; for example, the landscape mainly possesses negative entropy, but the regime holds positive entropy. (2) The power system's co-evolution entropy changes from 0.043 to −0.154, and reaches the maximum (0.054) in 2000s, which indicates that China's power system begins the coordinated development through a run-in. (3) The co-evolution of the power system is hindered by too much policy intervention; however, technology innovation and electricity market opening could enhance its co-evolution. We also derive strategic policy recommendations from the analysis result and present a co-evolution concept to improve the socio-technical system.