Price-based Unit Commitment Research Articles

Price‐based unit commitment with decision‐dependent uncertainty in hourly demand

The price-based unit commitment (PBUC) problem aims to optimise the power generating units' schedules to meet the system demand with the objective to maximise the generation companies' (GENCOs') profit. State-of-the-art PBUC models have taken into account exogenous uncertainties in renewable generation, demand, and price signals. This study proposes a novel PBUC problem formulation with endogenous or decision-dependent uncertainty (DDU) in the elastic portion of the demand. The proposed PBUC model is formulated as a mixed-integer non-linear programming (MINLP) problem with non-convex continuous relaxation. A concavification approach is developed to reformulate the non-convex MINLP model as an equivalent mixed-integer quadratic programming (MIQP) model whose continuous relaxation is convex. Case studies considering GENCOs owning and operating 3, 12, 19, and 40 generating units demonstrate the efficacy of the proposed DDU-aware PBUC formulation on the GENCOs' anticipated profits.

Stochastic optimization for price-based unit commitment in renewable energy-based personal rapid transit systems in sustainable smart cities

Power resources scheduling and planning have a great impact on today’s sustainable cities. The conventional power plants as well as renewable energy resources should be committed to considering social, technical, economic, and environmental sustainability optimally. On the other hand, transportation electrification is one of the key pillars of today’s smart cities. In this paper, the generation capacity is scheduled to meet system’s loadability using a new personal rapid transit system, which is carried out utilizing a price-based unit commitment (PBUC) that maximizes profit based on price signals in the context of a stochastic optimization scheme. The personal rapid transit system at the University of West Virginia is considered as a case study based on several prescribed planning scenarios. Wind power energy, as one of the popular renewable energy sources, is considered for power generation. The integrated simulation and optimization results confirm the effectiveness and robustness of the proposed PBUC methodology.

Systematic Categorization of Optimization Strategies for Virtual Power Plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

Economics of energy storage options to support a conventional power plant: A stochastic approach for optimal energy storage sizing

Is an energy storage system (ESS) an economic choice to enhance the flexibility and controllability of a conventional power plant (CPP)? So far, most literatures have investigated the profitability of ESSs beside renewable power plants (RESs). The focus of this paper is on economic assessment of an ESS alongside a CPP in a price taker generation company (GENCO). This work presents a method to determine economic storage technology options and their optimal power and energy capacities. A stochastic price based unit commitment (PBUC) conjugated with a stochastic price based storage commitment (PBSC) is developed in the ESS sizing problem. The output determines the optimal participation of a power plant and its optimum sized ESS in day-ahead energy and spinning reserve markets and bilateral contracts. Numerical results confirm the capability of the proposed model to improve the operational characteristics of the CPPs and to increase the profitability of GENCOs. The results show that the ESS increases the GENCOs' profit by up to 36%. The ESS, using the proposed model, can reduce the number of on line hours of the CPP about 13%. Also, the GENCO can offer about 10% lower price for bilateral contracts.

Stochastic price based coordinated operation planning of energy storage system and conventional power plant

A generation company (GENCO) which has a conventional power plant (CPP) intends to add an energy storage system (ESS) beside the CPP to increase its flexibility and profitability. For this purpose, a new model is proposed for coordinated operation planning of the CPP and ESS in energy and spinning reserve markets in the presence of a bilateral contract. The proposed model is based on the stochastic price based unit commitment (PBUC) and price based storage commitment (PBSC). The uncertainties of the energy and spinning reserve prices and delivery requests in the spinning reserve market are modeled via scenarios based upon historical data. The proposed model maximizes the profitability of the ESS beside the CPP and encourages the GENCO to invest the ESS. ESS technology options to use beside the CPP are determined by economic assessments. Numerical results show that utilization of ESSs improves the technical operation of CPPs, as well as GENCOs’ profitability.

Evaluati on of long-term start up costs impact on short-term price based operational optimization of a CCGT using MILP

An increasing share of the weather-dependent RES generation in the power system leads to the growing importance of flexibility of conventional power plants. They were usually designed for base load operation and it is a challenge to determine the actual long-term cycling costs, which account for an increase in maintenance and overhaul expenditures, increased forced outage rates and shortened life expectancy of the plant and components. In this paper, the overall impact of start up costs is evaluated by formulating and solving price based unit commitment problem (PBUC). The electricity spot market is considered as a measure for remunerating flexibility. This approach is applied to a real-life case study based on the 70 MWe PGE Gorzόw CCGT power plant. Different operation modes are calculated and results are used to derive a mixed integer linear programming (MILP) model to optimize the operation of the plant. The developed mathematical model is implemented in Python within the frame of the PuLP library and solved using GUROBI. Results of the application of the method to a numerical example are presented.

Price-based unit commitment electricity storage arbitrage with piecewise linear price-effects

Electricity storage plants can be used for many applications, with one of the most studied applications being arbitrage in the day-ahead market. Although the arbitrage value is related to the presence of price spreads, it also depends on the effect of (dis)charge actions on prices, as arbitrage generally reduces price spreads by increasing off-peak prices when charging and decreasing peak prices when discharging. As such, there are two important assumptions in price-based unit commitment arbitrage models: first, whether the storage operator is assumed to have perfect knowledge of future prices, and second, whether they recognize that their (dis)charge actions may affect those prices, i.e., the price-taking or price-making assumption. This article proposes a comprehensive formulation of the arbitrage problem including detailed operating constraints, and focuses on relaxing the price-taking assumption by considering real-world price-effect data, published in the form of hourly piecewise linear relationships between quantity and price based on submitted bids, which are referred to as “market resilience functions”. These can be used to (1) evaluate the price-taking and price-making assumptions based on simplified price-effects, and to (2) provide an upper limit to the arbitrage value under the assumption that prices and price-effects are known at the decision stage. In addition, a stepwise approximation to the piecewise linear functions is developed to reduce computation time, i.e., from mixed-integer nonconvex quadratic programming to mixed-integer linear programming, while providing lower- and upper bound approximations to the arbitrage value. The developed models are applied to the Belgian day-ahead market for 2014, and show that the price-effect has a strong impact on the operation and arbitrage value of large-scale storage.

Stochastic approach to represent distributed energy resources in the form of a virtual power plant in energy and reserve markets

Today, traditional networks are changing to active grids due to the burgeoning growth of distributed energy resources (DER), which demands scrupulous attention to technical infrastructures, as well as economic aspects. In this study, from economic point of view, the aggregation of DERs in a distribution network to participate in joint energy and reserve markets is investigated. This approach, which is predicated upon price-based unit commitment method, has considered virtually all the technical data in the proposed model. It is worth to mention that uncertainties of loads and market prices, as an inherent characteristic of the electricity markets, are treated in this study, and their effect on the operation of virtual power plants in energy and reserve markets has been thoroughly discussed. To this end, for both uncertain parameters, a good number of scenarios are generated and using the backward reduction method the number of these scenarios is reduced. The problem is formulated as a MINLP model and is implemented in GAMS software, while its authenticity is validated using particle swarm optimisation method.

Evaluation of an energy- and exergy-based generic modeling approach of combined heat and power plants

Reduced models of combined heat and power plants are required for different applications. Among other usages, they are implemented as mixed integer linear programs (MILP) in energy market models or price-based unit commitment problems to study the economic feasibility and optimal operation strategies of different units. Generic models are particularly useful when limited information is available for each considered plant. This paper presents a MILP modeling approach for combined heat and power (CHP) plants. The approach is based on energy and exergy balances and a few typical plant characteristics for different operating conditions. The reduction of electrical power output due to heat extraction is estimated by the transferred exergy to the district heating network. Furthermore, the accuracy, strengths and limitations of this approach are investigated for various CHP plant types with extraction condensing turbines designed for district heating systems. Therefore, detailed thermodynamic cycle simulations of CHP plants including part load operations are used to obtain the real plant operating conditions to compare them to the results of the described generic approach. The validation of the reduced, generic model shows that the accuracy mainly depends on the effectiveness of the heat extraction from the CHP plant. In addition, it can be seen that the main advantage of the presented exergy-based method is the inherent consideration of the feed flow temperature for the calculation of the power reduction due to heat extraction.

Arbitrage strategy of virtual power plants in energy, spinning reserve and reactive power markets

Virtual power plant (VPP) concept was developed to integrate distributed energy resources (DERs) into the grid in order that they are seen as a single power plant by the market and power system operator. Therefore, VPPs are faced with optimal bidding, and identifying arbitrage opportunities in a market environment. In this study, the authors present an arbitrage strategy for VPPs by participating in energy and ancillary service (i.e. spinning reserve and reactive power services) markets. On the basis of a security-constrained price-based unit commitment, their proposed model maximises VPP's profit (revenue minus costs) considering arbitrage opportunities. The supply–demand balancing, transmission network topology and security constraints are considered to ensure reliable operation of VPP. The mathematical model is a mixed-integer non-linear optimisation problem with inter-temporal constraints, and solved by mixed-integer non-linear programming. The result is a single optimal bidding profile and a schedule for managing active and reactive power under participating in the markets. These profile and schedule consider the DERs and network constraints simultaneously, and explore arbitrage opportunities of VPP. Results pertaining to an illustrative example and a case study are discussed.

Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects

Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects

Optimal operation and forecasting policy for pump storage plants in day-ahead markets

Pump storage plants are an important electricity storage technology at present. Investments in this technology are expected to increase. The necessary investment valuation often includes expected cash flows from future price-based unit commitment policies. A price-based unit commitment policy has to consider market price uncertainty and the information revealing nature of electricity markets. For this environment stochastic programming models are suggested to derive the optimal unit commitment policy. For the considered day-ahead price electricity market stochastic and deterministic unit commitment policies are comparable suggesting an application of easier implementable deterministic models. In order to identify suitable unit commitment and forecasting policies, deterministic unit commitment models are applied to actual day-ahead electricity prices of a whole year. As a result, a robust forecasting model should consider the unit commitment planning period. This robust forecasting models result in expected cash flows similar to realized ones allowing a reliable investment valuation.

Short-term Price-based Unit Commitment of Hydrothermal GenCos: A Pre-emptive Goal Programming Approach

Short-term Price-based Unit Commitment of Hydrothermal GenCos: A Pre-emptive Goal Programming Approach

Price-Based Unit Commitment With Wind Power Utilization Constraints

This paper proposes an optimal bidding strategy for independent power producers (IPPs) in the deregulated electricity market. The IPPs are assumed to be price takers, whose objectives are to maximize their profits considering price and wind power output uncertainties, while ensuring high wind power utilization. The problem is formulated as a two-stage stochastic price-based unit commitment problem with chance constraints to ensure wind power utilization. In our model, the first stage decision includes unit commitment and quantity of electricity submitted to the day-ahead market. The second stage decision includes generation dispatch, actual usage of wind power, and amount of energy imbalance between the day-ahead and real-time markets. The chance constraint is applied to ensure a certain percentage of wind power utilization so as to comply with renewable energy utilization regulations. Finally, a sample average approximation (SAA) approach is applied to solve the problem, and the computational results are reported for the proposed SAA algorithm showing the sensitivity of the total profit as the requirement of wind power utilization changes.

Stochastic Price-Based Coordination of Intrahour Wind Energy and Storage in a Generation Company

This paper develops a day-ahead price-based scheduling strategy for the coordination of wind and storage units in a generating company (GENCO). The proposed strategy is based on the stochastic price-based unit commitment (PBUC) for the scheduling of wind energy and storage units, which considers volatilities in day-ahead intrahour market prices and wind power generation. The proposed approach firms up the hourly sum of wind and storage unit generation, and mitigates potential wind energy imbalance charges for participating GENCOs in electricity markets. Although the proposed approach would apply to any kind of storage, we consider the pumped-storage (PS) hydro unit as an example in this study. Numerical examples illustrate a GENCO's day-ahead coordinated scheduling results for wind and PS units.

Price Based Unit Commitment Considering Fuzzy Uncertainty

In deregulated systems, bidding plays an important role for Gencos participating with the objective of maximizing profit. While making bidding strategies, factors like unit constraint and price uncertainty need to be considered as they have direct impact on the expected profit. The goal of the partnership units of solution is based on the price. By solving this problem, the generation company to determine the optimal generation schedule and unit status hourly. MCP plays an important role in the profitability of the units. In this paper, a novel approach to solving an optimal bid curve underprice uncertainty using PBUC in PAB markets is presented. Numerical results show the suitability of the method on a sample market.

Two-stage approach for optimal dispatch of distributed energy resources in distribution networks considering virtual power plant concept

SUMMARY With increasing levels of distributed generation (DG) penetration, especially variable output ones, new challenges have appeared in electric power systems. The outputs of these generators are variable and determined largely by nature of the power sources such as wind, wave, and sunlight. One solution to decrease problems caused by the variable output of these resources is to aggregate them into a virtual power plant (VPP). The VPP is an energy management system tasked to aggregate the capacity of some DGs, storage facilities, and dispatchable loads (DL) for the purpose of energy trading and/or providing system support services. The economically optimal control of distributed energy resources (DERs) included in the VPP is complicated by uncertainties, nonlinearities, and inter-temporal constraints. Therefore, a novel two-stage dispatch framework is presented here for the integration of the stochastic and the controllable DERs in the distribution network through the VPP concept. The first stage deals with the VPP optimal bidding model (VOBM). A probabilistic price-based unit commitment using point estimate method (PEM) is used in the VOBM to allow the VPP to decide on the unit commitment of its DERs and the optimal bids to the day-ahead market. The second stage deals with active distribution network management. The distribution system operator uses input from the VOBM to manage any local network constraints and to determine the characteristics of the entire local network at the grid supply points, while seeking to minimize the hourly operation cost. The proposed optimization algorithm is applied to an 18-bus system. Copyright © 2012 John Wiley & Sons, Ltd.

Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method

Environmental concerns, improvements in renewable energy technologies, governmental incentives for the use of these resources, and increased T&D costs, are the main factors driving the energy sector into a new era, where considerable portions of electrical demand will be met through widespread installation of Distributed Energy Resources (DERs). The Virtual Power Plant (VPP) is a decentralized energy management system tasked to aggregate the capacity of some Distributed Generations (DGs), storage facilities, and Dispatchable Loads (DLs) for the purpose of energy trading and/or providing system support services. Due to the stochastic behavior of the prime sources of some DGs, such as wind speed and temperature, the steady state analysis of the systems with integration of such DG units requires a probabilistic approach. In this paper, a probabilistic Price Based Unit Commitment (PBUC) approach using Point Estimate Method (PEM) is employed to model the uncertainty in market price and generation sources, for optimal bidding of a VPP in a day-ahead electricity market. Also, the uncertainty of stochastic DGs generations is handled through increasing the amount of required reserve. The proposed model allows a VPP to decide on the unit commitment of its DERs, and the optimal sale/purchase bids to the day-ahead market. The proposed optimization algorithm is applied to an 18-buses system.

Risk-Constrained Coordination of Cascaded Hydro Units With Variable Wind Power Generation

This paper presents a stochastic hourly coordination strategy for wind units and cascaded hydro generation as storage to firm up the hourly dispatch in a generating company (GENCO). The proposed strategy is based on the stochastic price-based unit commitment (Stochastic PBUC) formulation which includes wind energy imbalance charges. The forecast errors of electricity market price and wind speed are simulated with the Monte Carlo method via a scenario approach. The risk-aversion constraints are considered for limiting a GENCO's financial risks when considering uncertain wind power generation. The proposed optimization model is solved by mixed-integer linear programming (MIP) and illustrative examples examine the effectiveness of the proposed risk-based coordination model for optimizing a GENCO's payoff.

Generation risk assessment in volatile conditions with wind, hydro, and natural gas units

This paper studies a generating company (GENCO)’s midterm (a few months to a year) scheduling payoffs and risks in volatile operating conditions. The proposed algorithm considers the integration of intermittent wind units into a GENCO’s generation assets and coordinates the GENCO’s hourly wind generation schedule with that of natural gas (NG) units (with volatile gas prices) and hydro units (with water inflow forecast) for maximizing the GENCO’s payoff. The proposed midterm GENCO model applies market price forecasts to the risk-constrained stochastic price-based unit commitment (PBUC) for calculating the GENCO’s risk in energy and ancillary services markets. The proposed PBUC minimizes the cost of (a) NG contracts, storage, startup and shutdown, (b) startup and shutdown of cascaded hydro units, and (c) penalty for defaulting on the scheduled power delivery. Simulation results show that the diversification of generating assets including bilateral contracts (BCs) could enhance the GENCO’s midterm planning by increasing the expected payoff and decreasing the financial risk.