Electricity Spot Market Research Articles

Optimal dispatch of heterogeneous energy complementary system under energy indifference bidding

Owing to the influence of uncontrollable factors (e.g., climate), wind and photovoltaic (PV) energy are highly fluctuating and undispatchable, thereby hindering their integration to the grid. To address this issue, complementary systems using hydropower as a regulating energy are gradually emerging. With the development of wind and PV power industries and the electricity spot market, wind and PV power subsidies are being reduced, and the bidding subjects are diverse. It is likely that wind, PV, and hydropower will participate in the market with the same quality and price. Wind–photovoltaic–hydropower complementary systems (WPHCSs) normally aim at minimum wind and PV power curtailment, which do not consider hydropower production aspects. This study considers the effect of complementary operation on hydropower in the context of energy indifference bidding in the future electricity spot market, establishes a comprehensive benefit index system to optimize WPHCS, and proposes a particle swarm optimization algorithm that introduces the dominant solution with an adjustable parameter to solve the maximizing comprehensive benefits of a complementary system model. Control experiments with different loads, storage capacities, inflows, and models were conducted to verify the superiority of the proposed model and to summarize the optimal operation rule of the complementary system. The results show that turbine efficiency is the main factor affecting the results, and the total benefit of the system could be improved by up to 69% comparing with the priority consumption of wind and the PV power model. This paper describes the WPHCS benefits more comprehensively, and the results can be used to improve the benefits of complementary systems.

Equilibrium strategy based policy shifts towards the integration of wind power in spot electricity markets: A perspective from China

As part of China's power industry reforms, developing appropriate market designs for the inclusion of renewable energy in the electricity spot market has been an ongoing challenge. This paper examined the effects of different market rules on wind power producer behavior to identify the policy implications for a renewable-friendly spot market, for which a market equilibrium model was developed to assess power transactions in the day-ahead and balancing markets, with the upper level being wind power producers seeking the best profits, and the lower level being the market clearing process to minimize generation costs. A case study is given, in which different scenarios are explored to assess the effects of bidding regulations and imbalance pricing mechanisms and to simulate the rising wind penetration in power systems. It was found that in the region modeled: more relaxed bidding regulations could provide wind producers incentives by allowing more profitable strategies; the imbalance pricing settings influence market results and producer revenues; and lower spot prices and power generation costs occur if a larger wind share was fed into the power system and could result in a unit revenue decline. Therefore, new policy directions are needed for the development of China's renewable-friendly electricity markets.

Identification method of market power abuse of generators based on lasso-logit model in spot market

Accurate identification of violations of generators' market power abuse strongly guarantees that the electricity spot market operates smoothly. To reduce the collinearity of indicators and improve the model's performance, the least absolute shrinkage and selection operator (Lasso) is introduced into the binary logit model, and a method for identifying the market power abuse of generators based on the Lasso-logit model is proposed. First, an identification indicator system is constructed using three aspects: structural, behavioral, and impact indicators. Then, it is screened using the Lasso variable selection for identifying the market power abuse of generators. Based on these results, the Lasso-logit model for identifying the market power abuse of generators is established, and its performance is evaluated using the errors of two kinds and receiver operating characteristic (ROC) curve. Finally, the model is applied to an area's electricity spot market. The results show that the Lasso-logit model has a 96.6% correct rate and can be used to identify illegal generators of the market power abuse in the region. Indicators such as the high-price declaration rate, out-of-merit capacity index, marginal generator reaching the limit rate, market-clearing price and TOP-4 index have practical significance for identifying the market power abuse of generators.

Economic Dispatch of CHP Units through District Heating Network’s Demand-Side Management

Optimisation of heat and electrical load distribution, where the objective function is the maximum efficiency of the CHP unit for a given load range, can be done considering the limitations of electrical power and the heat load. Simulating a real CHP unit with a district heating network shows that demand-side management can improve the overall economic efficiency of the CHP plant and increase the unit’s operating range in the electricity spot market. Economic dispatch makes it possible to determine a reasonable additional increase in the electric power of the CHP unit, and to optimise the supply temperature and mass flow of the district heating network. The results obtained and the analysis performed indicate that the proposed methodology provides logical results and can be used to calculate the efficiency indicators of the cogeneration of electrical and thermal energy. The problem of optimising the operating mode of the CHP unit was solved, which allows us to determine the optimal additional increase in the unit’s electrical load at a given heat load of consumers, which on average increases the CHP unit’s efficiency up to an additional 1.5%.

High-Dimensional Mixed-Frequency IV Regression

This article introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonov-regularized estimator and study its large sample properties for time series data. The estimator has a closed-form expression that is easy to compute and demonstrates excellent performance in our Monte Carlo experiments. We also provide the confidence bands and incorporate the exogenous covariates via the double/debiased machine learning approach. In our empirical illustration, we estimate the real-time price elasticity of supply on the Australian electricity spot market. Our estimates suggest that the supply is relatively inelastic throughout the day.

Discussion on Electricity Spot Market Mode Adapting to Large-scale Renewable Energy Consumption

With the implementation of the “Thirteenth Five-Year Plan”, the scale of my country’s renewable energy installed capacity continues to expand, and the responsibility for consuming renewable energy power is becoming higher and higher. Promoting the reform of the electricity spot market provides a good opportunity to realize the consumption of large-scale renewable energy. This article aims at the northwest region where renewable energy accounts for a relatively high proportion in my country, and clarifies the factors that inhibit the consumption of renewable energy. Based on the investigation of the typical foreign power spot market model, a power spot market model suitable for large-scale renewable energy is proposed. According to the actual data of a province’s power grid in the northwest region, the day-ahead spot market under different models was cleared separately. The results show that the centralized market model is more suitable for large-scale renewable energy grid-connected consumption, and is conducive to promoting energy conservation and emission reduction and reducing power generation costs.

Analysis on the uncertainty factors of pumped storage power station construction investment under the Chinese characteristic power market environment

Pumped storage power stations provide security and support for the new power system with new energy as the main body. Pumped storage power stations are encouraged to participate in the spot electricity market and form electricity price in a competitive way. Based on the identification of the uncertain factors and the calculation of price fluctuation of the pumped storage power station participating in the electric power spot market with Chinese characteristics in the electric power market environment, this paper adopts the sensitivity analysis method to analyze the impact of the change rate of the price spread on the internal rate of return on capital of the pumped storage power station in the trading day, and draws the following conclusions according to the calculation examples:When the sensitivity coefficient is positive, the extraction spread is greater than the benchmark extraction spread by 25%, and with the increase of the extraction spread, the internal rate of return is greater.

A locational Marginal Price-Based Partition Optimal Economic Dispatch Model of Multi-Energy Systems

The large-scale penetration of renewable energy and deeply integrated multiple energy forms pose a major challenge for the accounting and allocation process of additional adjustment cost in multi-energy system and electricity market. The variable wind power and the operating status of multi-energy systems faced by the electricity market have driven researchers towards the decision of electricity spot market with complicated pricing mechanism. This paper presents a locational marginal price-based partition optimal economic dispatch model of multi-energy system with a high proportion of renewable energy. First, a model of the additional adjustment cost of multi-energy adjustable units to improve the power grid adjustment capacity is studied based on the power balance model of multi-energy conversion and storage. In the second, to improve the effectiveness of the ESM mechanism, an optimal economic dispatch model is established, with the goal of minimizing the additional adjustment cost of multi-energy adjustable units. Moreover, according to the characteristics of the model and the mechanism of locational marginal price-based economic dispatch, a solution method based on lagrangian relaxation is proposed. The incentive compatible locational marginal price is obtained. Finally, a simulation model of multi-energy systems based on IEEE 39-nodes power system and 7-nodes natural gas system is established. And the model is based on operating data of an actual grid with high proportion of renewable energy in Northeast China. The results of the examples show that the proposed method can effectively improve the efficiency and flexibility of multi-energy system. Another advantage of the proposed method is that the capabilities of the complementary multi-energy coordination can be promoted.

An effective Two-Stage Electricity Price forecasting scheme

With the development of the global power market reform, the monopoly of the power sector and government control pattern has gradually broken. Due to the unique properties of electricity, electricity prices show high volatility and uncertainty, bringing significant challenges to the accurate prediction of electricity prices. The sudden occurrence of a few spike prices in the electricity spot market has significantly affected electricity price forecasting accuracy. We propose a novel two-stage electricity price forecasting scheme (TSEP). A multi-source data-based spike occurrence prediction scheme is presented in the first stage, which adopts a deep neural network (DNN) to predict whether the price to be forecasted is a spike or not. Specifically, to alleviate the impact of low spike price samples, the oversampling method is used to synthesize some spikes at the data level. A loss function with a misclassification penalty to increase the cost of missing price spikes is designed at the algorithm level. Based on the outputs of the first stage, in the second stage, TSEP exploits the variance stabilizing transformations respectively suitable for pre-processing spike and normal prices and combines an artificial neural network (ANN) based spike calibration model to improve the accuracy of electricity price forecasting further. The experimental results on the European Power Exchange for France (EPEX-FR) demonstrate that TSEP increases spike occurrence prediction accuracy compared with the conventional models and significantly improves the accuracy of spike electricity price forecasting without affecting the accuracy of forecasting normal electricity price.

Research on Decision Optimization Model of Microgrid Participating in Spot Market Transaction

The development of electricity spot trading provides an opportunity for microgrids to participate in the spot market transaction, which is of great significance to the research of microgrids participating in the electricity spot market. Under the background of spot market construction, this paper takes the microgrid including wind power, photovoltaic (PV), gas turbine, battery storage, and demand response as the research object, uses the stochastic optimization method to deal with the uncertainty of wind and PV power, and constructs a decision optimization model with the goal of maximizing the expected revenue of microgrids in the spot market. Through the case study, the optimal bidding electricity of microgrid operators in the spot market is obtained, and the revenue is USD 923.07. Then, this paper further investigates the effects of demand response, meteorological factors, market price coefficients, and cost coefficients on the expected revenue of microgrids. The results demonstrate that the demand response adopted in this paper has better social–economic benefits, which can reduce the peak load while ensuring the reliability of the microgrid, and the optimization model also ensure profits while extreme weather and related economic coefficients change, providing a set of scientific quantitative analysis tools for microgrids to trade electricity in the spot market.

Trading Mechanism and Clearing Model of Incentive Demand Response in Electricity Spot Market

Trading Mechanism and Clearing Model of Incentive Demand Response in Electricity Spot Market

Smart Scheduling of Pump Control in Wastewater Networks Based on Electricity Spot Market Prices

Smart Scheduling of Pump Control in Wastewater Networks Based on Electricity Spot Market Prices

Identification of Generating Units That Abuse Market Power in Electricity Spot Market Based on AdaBoost-DT Algorithm

The identification of generating units that abuse market power is an essential part of risk prevention in a spot market, especially in the early stage of the construction of the spot market. In this study, a model for identifying generating units that abuse market power is designed based on the AdaBoost-DT algorithm. It is targeted at the imbalance between samples of generating units that abuse market power and normal generating units in the spot market. First, the four main methods by which market power is abused by generating units in the spot market are described: collusion, economic withholding, physical withholding, and extreme quotation. Second, the specific characteristics of the four methods are analyzed, and the identification indexes for generating units that abuse market power are established. Thereafter, a sample set of generating units that abuse market power using different methods is constructed. Furthermore, a training set is formed with samples of normal generating units to construct a model based on the AdaBoost-DT algorithm, for identifying generating units that abuse market power. Finally, the spot market data of a certain region are used for an example analysis. The results show that the accuracy of model identification is 97%, which validates the method.

Spot electricity market design for a power system characterized by high penetration of renewable energy generation

AbstractThe continuous growth of renewable generation in power systems brings serious challenges to electricity markets due to their characteristics different from conventional generation technologies. These challenges come from two dimensions, including short‐term (energy and ancillary service markets) and long‐term (long‐term bilateral and capacity markets) aspects. Under this background, the design of energy and ancillary service markets is studied for power systems with a high penetration level of variable renewable generation. In the proposed spot market mechanism, energy and frequency regulation service (FRS) bids are jointly cleared, where renewable generators are motivated to proactively manage the intermittency and uncertainty of their power outputs. The proposed market mechanism can also ensure the adequacy of FRS capacity for compensating variability of renewables. Besides, in order to ensure the execution of spot market clearing outcomes, this paper established a penalty scheme for mitigating the real‐time fluctuations of renewable generation outputs in the spot market. Differences between real‐time generation outputs and market clearing outcomes are managed within a certain limit by imposing the designed penalty prices on deviations. Finally, the feasibility and efficiency of the developed market mechanism and algorithms are manifested in the case studies.

Electricity price modelling with stochastic volatility and jumps: An empirical investigation

Over the past few years, the electricity derivatives market has experienced a substantial growth in the volume of trade and the diversity of available products. This has led to a rich data environment that requires more sophisticated and accurate modelling approaches for electricity spot prices. This paper deals with an analysis of continuous-time stochastic volatility jump-diffusion processes in the context of pricing of futures contracts written on electricity spots. We formulate a variety of models which aim to capture the most prominent characteristics and stylised facts of the electricity spot market including mean reversion, seasonality, extreme volatility, and spikes. The proposed modelling framework extends the existing models by incorporating mean reversion, stochastic volatility, and jumps in both the underlying spot price process and its volatility. The modelling parameters are estimated using the Markov Chain Monte Carlo (MCMC) technique for the Australian electricity market. We find that incorporating stochastic volatility and jumps in both the underlying electricity spot price and its volatility is absolutely essential to accurately fit the observed electricity spot prices. We derive futures prices in a semi-closed form and confirm flexibility of the proposed models by their ability to fit the observed spot and futures prices in the Australian electricity market.

Competitive model of pumped storage power plants participating in electricity spot Market——in case of China

With the development of transmission and distribution price reform in China, pumped storage power station can not continue to be included in the effective assets of the power grid, and its cost can not be dredged through the transmission and distribution price, so it is urgent to find a way to protect its own income through the market. This paper innovatively proposes a “three-stage” competitive optimization model for pumped-storage power stations, using a quadratic programming algorithm with two consecutive iterations to convert the discrete programming problem into a linear convex programming problem, reducing the difficulty of calculation and improving the calculation accuracy. Finally, the reinforcement learning algorithm is used to obtain the real-time bidding strategy of the pumped storage power station, and continuous feedback is provided. The calculation example analysis shows that compared with the traditional model, the “three-stage” model can bring better benefits to the pumped storage power station, and when the actual value of demand fluctuates within −8%, the pumped storage power station has the ability to resist risks higher than the market average. And when the proportion of renewable energy increases from the current 8%–30%, the revenue of pumped storage power plants will drop by 20%.

Analysis on the operation mechanism of spot mode adapting to the initial stage of China’s electricity spot market

In the new round of power system reform, the construction of power market is the top priority. It is particularly important to design a reasonable power market construction path for the smooth transition from planned mode to market mode and finally achieve the target market mode. This paper makes a comparative analysis of the decentralized market and the centralized market, and further analyses its applicability and the necessity of medium and long-term contracts according to its characteristics. It proposes to coordinate the generation resources, grid structure and load resources within the jurisdiction of the power grid, and implement the decentralized power market mode for the parts that have the ability to achieve self-balance, that is, the physical execution of medium and long-term contracts; For some units with large deviation and without physical execution or for some period of time, the centralized power market mode is implemented. The specific mode division adopts the principle of adjusting measures to local conditions, and strives to realize the smooth transition of market construction.

Preliminary Exploration of Establishing a Spot Quotation Alliance for Cascade Hydropower Stations

For the spot electricity market, the quotation combining the hydropower stations is more resistant to uncertainties of runoff and spot price in market bidding. Therefore, it is of great significance to set up a spot quotation system for these hydropower stations. The paper first studied the fundamental characteristics of hydropower stations at different tiers, based on which the establishment of a quotation system of hydropower stations at different tiers and the bidding procedures were proposed. In addition, the paper investigated the joint bid model suitable for hydropower stations at different tiers based on cooperation between bidding stations. Finally, the paper established a coordinated management model and conflict-solving model for the quotation system of hydropower stations at different tiers, providing theoretical support for the system to take part in the spot electricity market and improve its marketing capabilities.

Carbon pass-through rates on spot electricity prices in Australia

This study assesses the impact of the Australian Carbon Pricing Mechanism (CPM) on electricity prices in the National Electricity Market (NEM). An econometric model is designed to examine the influence of short-run marginal costs, electricity demand and emission intensities on carbon pass-through to spot electricity prices. Our sample period from July 2009 to June 2016 allows us to analyse three different sub-periods in order to quantify pass-through effects: the period before the CPM, the period when the pricing mechanism was effective, as well as the period after its repeal. Therefore, our study allows a comprehensive evaluation of the effect of carbon pricing on the Australian electricity spot market. Our results demonstrate a high degree of carbon costs pass-through across the considered markets, while the increase in electricity prices typically exceeds carbon costs, in particular for the markets in New South Wales, Queensland and South Australia. Furthermore, we find a negative relationship between carbon pass-through rates and emission intensities in the regional markets: pass-through rates were relatively low for Victoria that is characterised by electricity generation from fossil fuels and high emission intensities, while they were the highest for South Australia, a market with the lowest emission intensities and a higher share of renewable energy generation. We also find that for most markets, emission intensities were only reduced by a relatively small margin during the tax period and electricity prices have never reverted back to their pre-tax levels after the repeal of the tax. • The impact of the Carbon Pricing Mechanism (CPM) on Australian spot electricity prices is examined. • Results demonstrate that consumers were bearing the costs of carbon rather than generators. • Carbon costs pass-through estimates range from 100% to 290% across regional markets. • Evidence for a minor reduction in emission intensities during the CPM period is found. • Spot prices did not revert back to their pre-CPM levels after abolishment of the CPM.

Game analysis of decreasing generation cost characteristics in electricity spot market

The construction of Chinese electricity spot market is in its infancy. Market rules have borrowed from foreign mature market experiences, adopted marginal electricity price settlement mechanisms, and stipulated that the biddings on the power generation side will increase monotonously. However, there are thermal power generating units in China whose marginal cost does not meet the characteristics of monotonous increase. In this context, studying the strategic behaviour of thermal power generating units with diminishing marginal cost characteristics in the market, highlighting the impact of existing rules on market participants’ decision making, is of great significance to the design and operation of Chinese electricity spot market. First, an existing generation market clearing model based on monotonically increasing quotes and a strategy model for generating units based on monotonically decreasing marginal costs are established. Then, the above models are organically combined to form a bilevel optimization model, and the iterative optimization is performed to obtain a market equilibrium solution. Finally, a numerical example is used to clearly describe the bidding strategy of the unit in the spot market, and the incentive effects of market regulations on its bidding behaviour are discussed.