Day-ahead Electricity Market Research Articles

A Robust Day-Ahead Electricity Market Clearing Model Considering Wind Power Penetration

With the development and grid-connection of renewable energy sources such as wind power, there are more and more uncertainties in power systems, and large-scale wind power has brought many challenges to the security and stability the systems. These uncertainties have to be eliminated by means of upward or downward regulations of conventional generators and charging/discharging services of energy storage devices. Based on the analysis of the influence of wind power uncertainties on the day-ahead electricity market, this article proposes a robust clearing model for the day-ahead electricity market considering the wind power penetration, which can help to complete the dispatch of power system. Compared with the traditional models, the proposed model is a multi-objective model, which considers both the lowest operating cost and the least wind power curtailment of power system. Moreover, the obtained dual multipliers λ corresponding to the power balance constraints reflect the marginal cost of the power production in a certain period, that is, the locational marginal price (LMP), which can be used as the clearing prices. This robust market clearing model takes into consideration the economic and reliability of the system operation and accommodates as much renewable energy as possible. The simulation of three wind power producers was implemented on the IEEE 30-bus test system, which verified the rationality of the proposed approaches.

Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method.

Interactive Robust Model for Energy Service Providers Integrating Demand Response Programs in Wholesale Markets

Demand response (DR) has become a key player in energy efficiency programs of current electric power systems. This paper presents an effective decision-making model for energy service providers with a focus on day-ahead electricity market participation and demand allocation in the distribution network. A two-step iterative framework is proposed to consider the mutual effects of the DR and market prices, in which unit commitment and economic dispatch problems are solved in the first step to determine the locational marginal prices, and successively DR program is applied in the second step to minimize the total cost of providing energy for distribution network customers. This total cost includes the cost of power purchase from the market and distributed generation units, incentive cost paid to customers, and compensation cost of power interruptions. To consider the unexpected behaviors of other market participants, prices are modeled as uncertainty parameters using the robust optimization technique. Simulation results demonstrate the significant benefits of the proposed framework for the strategic performance of energy service providers.

Strategic bidding for a price-maker hydroelectric producer: Stochastic dual dynamic programming and Lagrangian relaxation

ABSTRACTIn bid-based markets, energy producers seek bidding strategies that maximize their revenue. In this article, we seek the maximum-revenue bidding schedule for a single price-maker hydroelectric producer. We assume the producer sells energy in the day-ahead electricity market and has the ability to impact the market-clearing price with its bids. To obtain the price-maker hydroelectric producer’s bidding schedule, we use a combination of Stochastic Dual Dynamic Programming and Lagrangian relaxation. In this framework, we dualize the water balance equations, allowing an exact representation of the non-concave immediate revenue function, while preserving the concave shape of the future revenue function. We model inflow uncertainty and its stagewise dependence by a periodic autoregressive model. To demonstrate our approaches’ utility, we model Honduras’ electricity market assuming that the thermal producers act as price-takers and that one price-maker hydro producer operates all of the hydroelectric plants.

On Charging Scheduling Optimization for a Wirelessly Charged Electric Bus System

The introduction of wirelessly charged electric buses (WCEBs) into current public transportation system attracts many attentions in recent years. As the wireless charging technology enables energy transfer from power transmitters to electric vehicles (EVs) on road, it provides a promising solution to reduce the huge cost of battery with large size and long charging time, which are two critical impediments for EV applications. However, the system cost of WCEBs is huge. Under the dynamic electricity demands and the fluctuating electricity prices, the system operating electricity cost highly depends on the charging schedule. In this paper, according to the typical day-ahead electricity market, we explore an optimal charging scheduling scheme in a WCEB system to minimize the system operating electricity cost, while the characteristic of WCEBs is considered. The price of electricity fluctuates with the accumulated energy demands in both spatial and temporal domains. We first present a day-ahead reserved wholesale electricity determination algorithm, in which, the average speeds of WCEBs are presumed. Then, we propose an optimal charging scheduling algorithm, in which the WCEB charging schedules in slots are optimized sequentially. Both the reserved electricity and the predicted speeds in the slot are used. Simulation results demonstrate the efficiency of our proposed WCEB charging schedules.

Corrections to “Purchase Bidding Strategy for a Retailer With Flexible Demands in Day-Ahead Electricity Market” [May 17 1839-1850

Presents corrections to the paper, “Purchase bidding strategy for a retailer with flexible demands in day-ahead electricity market,” (Song, M. and Amelin, M.), IEEE Trans. Power Syst., vol. 32, no. 3, pp. 1839–1850, May 2017.

A Bayesian Inference Approach to Unveil Supply Curves in Electricity Markets

With increased competition in wholesale electricity markets, the need for new decision-making tools for strategic producers has arisen. Optimal bidding strategies have traditionally been modeled as stochastic profit maximization problems. However, for producers with non-negligible market power, modeling the interactions with rival participants is fundamental. This can be achieved through equilibrium and hierarchical optimization models. The efficiency of these methods relies on the strategic producer's ability to model rival participants’ behavior and supply curve, but a substantial gap remains in the literature on modeling this uncertainty. In this study, we introduce a Bayesian inference approach to reveal the aggregate supply curve in a day-ahead electricity market. The proposed algorithm relies on Markov Chain Monte Carlo and sequential Monte Carlo methods. The major appeal of this approach is that it provides a complete model of the uncertainty of the aggregate supply curve, through an estimate of its posterior distribution. We show on a small case study that we are able to reveal accurately the aggregate supply curve with no prior information on rival participants. Finally, we show how this piece of information can be used by a price-maker producer in order to devise an optimal bidding strategy.

Corrections to “price-maker bidding in day-ahead electricity market for a retailer with flexible demands” [mar 18 1948-1958

Presents corrections to the paper, “Price-maker bidding in day-ahead electricity market for a retailer with flexible demands,” (Song, M. and Amelin, M.), IEEE Trans. Power Syst., vol. 33, no. 2, pp. 1948–1958, March 2018.

A multi-objective market-driven framework for power matching in the smart grid

Smart grids, to facilitate the electricity production, distribution, and consumption, employ information and communication technologies simultaneously. Electricity markets, through stabilizing the electricity prices, attempt to alleviate the challenges of power exchange. On one hand, buyers, by considering their full demand satisfaction, endeavor to purchase the electricity cost-effectively. On the other hand, sellers, by taking their limited electricity generation capacity into account, are interested in increasing their financial benefits. To address this challenge, this paper introduces a highly-functional semi-decentralized power matching framework based on multi-objective optimization techniques executing in a day-ahead electricity market. A two-stage price updating mechanism to continuously balance the electricity prices is also provided. At each time interval, buyers and sellers submit their individual electricity price offers to the market operator. The market operator tunes them and then, announces the electricity market price. A robust multi-objective power matching algorithm is developed to make the matching contracts considering buyers’ and sellers’ objectives along with grid stability constraints imposed by distribution system operators. It also considers the minimization of electricity distribution loss in the matching procedure. Simulation results indicate that the framework successfully reaches a reasonable balance of aforementioned conflicting objectives while conducing negotiating electricity price offers to an equilibrium. It is shown that the proposed algorithm behaves better compared to well-known multi-objective evolutionary algorithms in terms of both optimizing the social welfare and computational complexity (scalability). Finally, effects of the two-stage price updating mechanism on the stability of the proposed evolutionary algorithm is discussed. Performance comparisons show that the proposed framework outperforms the similar approaches available in the literature.

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.

Optimal Scheduling for Prosumers in Coupled Transactive Power and Gas Systems

To help the integration of various distributed energy resources (DERs), this paper proposes a transactive approach to the optimal scheduling for prosumers in coupled energy systems. DERs are coordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity market, as well as the wholesale gas market. In the day-ahead electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time balancing market, a VPP adjusts DER schedules to minimize imbalance costs. In this paper, we address the energy conversions between electric power and gas loads and investigate the interacting operations of gas and power systems. Our simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy systems.

Risk premia in the German day-ahead electricity market revisited: The impact of negative prices

This paper replicates the study by Viehmann (2011) that investigated risk premia in the German day-ahead electricity market from October 2005 to September 2008. While estimated sizes of risk premia can be replicated, this paper does not reproduce respective standard errors, leading to remarkable differences between the reported significance levels. An extension with data of preceding years points to further differences with respect to size and statistical significance. In addition, this paper analyzes the impact of negative prices on risk premia. Negative electricity prices were introduced in 2008 at the European Power Exchange (EPEX), and in 2013 at the Energy Exchange Austria (EXAA). The results of an econometric analysis suggest that the introduction of negative prices has led to a decrease in risk premia when compared to the period of a positive price regime.

Optimal operation of aggregated electric vehicle charging stations coupled with energy storage

Charging stations are the basic infrastructure for accommodating the energy needs of electric vehicles (EVs). Companies are expected to invest in these charging stations by installing them at locations with a dense concentration of vehicles, such as parking places, commercial centres, and workplaces. In order for investors in EV charging stations to maximise their profits and mitigate the impact on the power grid, these stations would benefit from coupling with an energy storage system (ESS). ESS would be used to arbitrage energy and to balance out the time-variant and uncertain EV energy demand. This study proposes a framework to optimise the offering/bidding strategy of an ensemble of charging stations coupled with ESS in the day-ahead electricity market. The proposed framework accounts for degradation of the ESS, robust scheduling against price uncertainty, as well as stochastic energy demand from EVs. The results show the viability of the proposed framework in providing cost savings to an ensemble of EV charging stations.

Price-Maker Bidding in Day-Ahead Electricity Market for a Retailer With Flexible Demands

This study develops a short-term planning model to determine the bidding curves on a day-ahead market for a price-maker retailer with flexible power demand. It concerns the interactions between the spot price and the flexible demand. Both conditional value-at-risk and volume deviation risk are taken into account. A numerical study using the data from the Nordic electricity market is performed to investigate the influence of risk factors on the retailer's profit, risk levels, average spot price, and total consumption. Three types of price elasticity are compared to show that the retailer can benefit from the flexibility in demand side in some cases. The flexibility also leads to lower spot prices so that the customers in real-time price-based demand response can face a lower electricity price for per-unit power consumption.

Day-Ahead Schedule and Equilibrium for the Coupled Electricity and Natural Gas Markets

With the increasing utilization of natural gas-fired power plants, efficiency and even reliability challenges on both industries arise from the growing reliance on natural gas as fuel in the electricity sector and the increasing demand fluctuation in the natural gas sector. Following a brief review of the discontinuities, improvements are discussed to coping with the desired timely and cost-effectively gas supply in the electricity day-ahead market scheduling. Inspired by its successful implantation in the electricity market, the locational marginal pricing mechanism is applied to the natural gas day-ahead market. Moreover, the bidding strategy for general gas demand utilities is provided while considering the potential applicability of demand response (DR) which is also practiced in the electricity market. The formulations are represented as bilevel problems of each corresponding market, of which upper level problems maximize the participants’ profit, while the lower level problem renders the market clearing with schedules and locational marginal prices. Then, a coevolutionary algorithm is proposed to find the equilibrium of the two coupled markets with integrated participants. Results show that the proposed methodology with synchronized intraday time intervals and DR abilities could provide higher efficiency for the natural gas market, which will help the electricity market better coordinated to provide flatter price signals while scheduling more renewable energy sources.

A stochastic programming approach for the optimal management of aggregated distributed energy resources

The paper focuses on the optimal management of distributed energy resources aggregated within a coalition. The problem is analyzed from the viewpoint of an aggregator, seen as an entity called to optimize the available resources so to satisfy the aggregated demand by eventually trading in the Day-Ahead Electricity Market. Both a full and a residual perspective in the management of the integrated resources is investigated and compared. The inherent uncertainty affecting the optimal decision problem, mainly related to the demand profile, electricity prices and production from renewable sources, is dealt by adopting the two-stage stochastic programming paradigm. The proposed models (different for the full and residual case) present a bi-objective function, integrating the expected profit and a risk measure, the Conditional Value at Risk, to control undesirable effects caused by the random variations of the uncertain parameters. A broad numerical study has been carried out on real case study. The analysis of the results clearly shows the benefits deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion.

Event-based scheduling of industrial technical virtual power plant considering wind and market prices stochastic behaviors - A case study in Iran

The Virtual Power Plants containing Distributed Energy Resources are classified into two main categories of Commercial Virtual Power Plant and Technical Virtual Power Plant as a suitable way to manage industrial environments. Here, Industrial Technical Virtual Power Plant is defined as a scheduling unit containing loads and generations located in an industrial grid. A comprehensive framework is proposed here for normal and contingency conditions for various Virtual Power Plants participating in a short-term market. This framework performs a day-ahead and intra-day generation scheduling by selecting the best Demand Response programs. In this framework, the wind generations and the day-ahead and intra-day electricity market prices are considered as the stochastic parameters. A risk-management aspect is noticed in the proposed stages for contingency conditions. Then, some element changes such as seasonal load change and single-line outage are trained in the system to accredit the proposed solution in the contingency condition. Hereof, an appropriate technique is defined to represent the proposed model and solution. To specify the effectiveness and efficiency of the proposed methodology, the modified Isfahan Regional Electric Power Company network in Iran is experimented to test the method and to assess some encouraging aspects as well. By the proposed approach, attractiveness of Demand Response programs is revealed in industrial grids and the lower cost will be imposed. Also the improvement percentage of load shedding can be gained by performing the proposed scheduling that is so important for industrial processes.

Stochastic optimal generation bid to electricity markets with emissions risk constraints

There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the unit's optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERP's effects on the expected profits and the optimal generation bid.

Impacts of Premium Bounds on the Operation of Put Option and Day-ahead Electricity Markets

In this paper‎, ‎the impacts of premium bounds of put option contracts on the operation of put option and day-ahead electricity markets are studied‎. ‎To this end‎, ‎first a comprehensive equilibrium model for a joint put option and day-ahead markets is presented‎. ‎Interaction between put option and day-ahead markets‎, ‎uncertainty in fuel price, impact of premium bounds, and elasticity of consumers to strike price‎, ‎premium price, and day-ahead price are taken into account in this model‎. ‎Then, a new method for put option pricing is proposed. By applying the presented model to a test system, the impacts of premium bounds on equilibrium of joint put option and day-ahead markets are studied‎.

A probabilistic method for cost minimization in a day-ahead electricity market considering wind power uncertainties

During recent decades, using wind power as one of the most substantial renewable energy sources has been vastly intensified in power systems. Increasing wind power penetration in the power grid can have both detrimental and beneficial effects. Due to the uncertain nature of wind power, the system operator is faced with many challenges to make proper decisions for the electricity market. In this paper, a probabilistic method is proposed which not only effectively manages wind power sources along with other producers, but also takes the uncertain behavior of wind power in defined intervals into account. Using wind farm information in each hour, a new approach for calculating the probability density function (PDF) in uncertainty bounds is presented. By utilizing the calculated PDF two objective functions are proposed for minimizing the total expected cost during 24 h without and with considering consumers payments. The proposed objective functions have been minimized through a two-level optimization method using three different heuristic algorithms (Teaching-Learning-Based Optimization (TLBO), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA), and Ant Lion Optimization (ALO)) on an 8-bus sample transmission network. The results show the total expected cost has been decreased up to 20% in some scenarios. In addition, the proposed method can satisfy consumers by keeping their payments close to the lowest amount according to the different scenarios.