Unit commitment problem in deregulated environment

This paper addresses the self-scheduling problem of generation companies owning thermal power units considering bilateral contracts and day-ahead market. This approach allows precise modelling of variable costs, start-up costs and comprehensive system of constraints. The self-scheduling model is formulated as deterministic optimization problem in which expected profit is maximized by 0/1 mixed-integer linear programming technique. Solution is achieved using the homogeneous and self-dual interior point method for linear programming, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model for optimizing the thermal generation schedule is demonstrated through the case study with detailed discussion.     

Congestion management solution under secure bilateral transactions in hybrid electricity market for hydro-thermal combination

The deregulation of power system has created an environment of competitiveness among different market players and the transmission lines are forced to operate near to their thermal or stability limits. It is a challenge with System Operators (SO) to ensure a secure and reliable transmission of power under these conditions. This paper proposes a rescheduling based congestion management strategy in hybrid (pool + bilateral) electricity market structure for a combination of hydro and thermal units. The proposed congestion management problem has been formulated as mixed integer nonlinear programming (MINLP) problem with an objective to minimize the congestion management cost by suitably rescheduling the hydro and thermal units based on their up and down generation cost bids. The hydro units having lowest operational cost and fast startup time have been used to alleviate the congestion by considering non-concave piecewise linear performance curves for them. The secure bilateral transactions have been ensured while rescheduling of the generators for alleviating the congestion. The performance of the proposed model has been demonstrated by solving the congestion management problem on modified IEEE-24 bus system.     

Stochastic multiobjective self-scheduling of a power producer in joint energy and reserves markets

This paper presents a stochastic multiobjective model for self-scheduling of a power producer which participates in the day-ahead joint energy and reserves markets. The objective of a power producer is to compromise the conflicting objectives of payoff maximization and gaseous emissions minimization when committing its generation of thermal units. The proposed schedule will be used by the power producers to decide on emission quota arbitrage opportunities and for strategic bidding to the energy and reserves market. The paper analyzes a scenario-based multiobjective model in which random distributions, such as price forecasting inaccuracies as well as forced outage of generating units are modeled as scenarios tree using a combined fuzzy c-mean/Monte-Carlo simulation (FCM/MCS) method. With the above procedure the stochastic multiobjective self-scheduling problem is converted into corresponding deterministic problems. Then a multiobjective mathematical programming (MMP) approach based on ?-constraint method is implemented for each deterministic scenario. Piecewise linearized fuel and emission cost functions are applied for computational efficiency and the model is formulated as a mixed-integer programming (MIP) problem. Numerical simulations for a power producer with 21 thermal units are discussed to demonstrate the performance of the proposed approach in increasing expected payoffs by adjusting the emission quota arbitrage opportunities.     

Possibilistic programming approach for mid-term electric power planning in deregulated markets

The mid-term electric power planning problem under uncertainty for a generation company (GenCo) in a deregulated market having some thermal, hydro and wind power plants is addressed. We propose a novel possibilistic price-based mixed integer linear programming approach to solve the considered problem. Because the uncertainties especially those are related to market could not usually be based upon probabilities, the possibilistic distribution functions are used to simulate some key imprecise/ambiguous parameters: GenCo’s electricity market share; electricity, fuel and emission market prices; possible wind electric power; availability of generating units; available water reservoir for hydro units. To solve the proposed model, we develop an interactive possibilistic solution method based upon two recent solution approaches. Finally, we use a real-world case study to show the ability and appropriateness of our model and report computational results. Notably, the proposed approach can be easily applied for a regulated environment.     

日前电能市场与深度调峰市场联合出清模型

以采用全电量集中竞价模式的日前电能交易和东北深度调峰交易融合运行、联合出清为背景,建立一种日前电能量市场和深度调峰服务市场联合出清模型.模型以系统电能量费用、启动费用、调峰费用与新能源限电惩罚费用之和最小为目标函数,以系统电力平衡与备用、火电机组和新能源2类发电单元运行边界为约束条件,实现新能源优先消纳基础上的2市场整...     

Stochastic Joint Optimization of Wind Generation and Pumped-Storage Units in an Electricity Market

One of the main characteristics of wind power is the inherent variability and unpredictability of the generation source, even in the short-term. To cope with this drawback, hydro pumped-storage units have been proposed in the literature as a good complement to wind generation due to their ability to manage positive and negative energy imbalances over time. This paper investigates the combined optimization of a wind farm and a pumped-storage facility from the point of view of a generation company in a market environment. The optimization model is formulated as a two-stage stochastic programming problem with two random parameters: market prices and wind generation. The optimal bids for the day-ahead spot market are the ldquohere and nowrdquo decisions while the optimal operation of the facilities are the recourse variables. A joint configuration is modeled and compared with an uncoordinated operation. A realistic example case is presented where the developed models are tested with satisfactory results.     

Scheduling of hydrothermal power systems

The authors present a method for scheduling hydrothermal power systems based on the Lagrangian relaxation technique. By using Lagrange multipliers to relax system-wide demand and reserve requirements, the problem is decomposed and converted into a two-level optimization problem. Given the sets of Lagrange multipliers, a hydro unit subproblem is solved by a merit order allocation method, and a thermal unit subproblem is solved by using dynamic programming without discretizing generation levels. A subgradient algorithm is used to update the Lagrange multipliers. Numerical results based on Northeast Utilities data show that this algorithm is efficient, and near-optimal solutions are obtained. Compared with previous work where thermal units were scheduled by using the Lagrangian relaxation technique and hydro units by heuristics, the new coordinated hydro and thermal scheduling generates lower total costs and requires less computation time     

Mixed integer programming of generalized hydro-thermal self-scheduling of generating units

This paper presents the application of mixed-integer programming (MIP) approach for solving the hydro-thermal self scheduling (HTSS) problem of generating units. In the deregulated environment, the generation companies schedule their generators to maximize their profit while satisfying loads is not an obligation. The HTSS is a high dimensional mixed-integer optimization problem. Therefore, in the large-scale power systems, solving the HTSS is very difficult. In this paper, MIP formulation is adopted for precise modeling of dynamic ramp rate limits, prohibited operating zones, operating services, valve loading effects, variable fuel cost, non-linear start-up cost functions of thermal units, fuel and emission limits of thermal units, multi head power-discharge characteristics of hydro plants and spillage of reservoir. The modified IEEE 118-bus system is used to demonstrate the performance of the proposed method.     

Short-term operation scheduling of a hydropower plant in the day-ahead electricity market

This paper presents a novel approach to solve the short-term operation scheduling problem of a hydropower plant that sells energy in a deregulated electricity market with the objective of maximizing its revenue. This paper proposes a nonlinear programming based scheduling model that determines both the optimal unit commitment (start-ups and shut-downs scheduling) and the generation dispatch of the committed units (hourly power output). The power generated by each hydro unit is considered as a nonlinear function of the water discharge and the volume of the associated reservoir. The dependence of the units’ operating limits (maximum and minimum water flows) on the actual gross head has been also taken into account in this model. The results from a case study are also presented to illustrate the application of the proposed approach in a real hydro plant.     

Short-term hydrothermal generation scheduling model using a genetic algorithm

A new model to deal with the short-term generation scheduling problem for hydrothermal systems is proposed. Using genetic algorithms (GAs), the model handles simultaneously the subproblems of short-term hydrothermal coordination, unit commitment, and economic load dispatch. Considering a scheduling horizon period of a week, hourly generation schedules are obtained for each of both hydro and thermal units. Future cost curves of hydro generation, obtained from long and mid-term models, have been used to optimize the amount of hydro energy to be used during the week. In the genetic algorithm (GA) implementation, a new technique to represent candidate solutions is introduced, and a set of expert operators has been incorporated to improve the behavior of the algorithm. Results for a real system are presented and discussed.     

Optimal self-scheduling of a dominant power company in electricity markets

This paper addresses the problem of the self-scheduling of a power company with a dominant role in both the production and retail sectors of an electricity market. An integrated 0/1 mixed integer linear programming (MILP) formulation is provided, which combines both thermal and hydro subsystems in a single portfolio for a dominant power company through a detailed modeling of the operating constraints of thermal units and hydroplants. Residual demand curves for energy and reserves are used to model the effect of the power company’s interactions with its competitors. Test results on a medium-scale real test system address the effect that the power company’s forward commitments and the market rules have on its daily self-scheduling and profits as well as on the resulting energy and reserve market clearing prices.     

Optimal power scheduling of thermal units considering emission constraint for GENCOs’ profit maximization

In this paper, authors propose a novel method to determine an optimal solution for profit based unit commitment (PBUC) problem considering emission constraint, under a deregulated environment. In a restructured power system, generation companies (GENCOs) schedule their units with the aim of maximizing their own profit by relaxing demand fulfillment constraints without any regard to social benefits. In the new structure, due to strict reflection of power price in market data, this factor should be considered as an important ingredient in decision-making process. In this paper a social-political based optimization algorithm called imperialist competitive algorithm (ICA) in combination with a novel meta-heuristic constraint handling technique is proposed. This method utilizes operation features of PBUC problem and a penalty factor approach to solve an emission constrained PBUC problem in order to maximize GENCOs profit. Effectiveness of presented method for solving non-convex optimization problem of thermal generators scheduling in a day-ahead deregulated electricity market is validated using several test systems consisting 10, 40 and 100 generation units.     

Short-Term Hydrothermal Power System Unit Commitment: A Comparative Study

To supply the required load, most electric power systems contain a combination of hydro and thermal power plants. The aim of this work is to study the optimal short-term operation of this combination, where the objective function is to minimize the thermal fuel cost and at the same time satisfy the hydro and thermal constraints, such as power balance at every hour, available water resources, reservoir volumes, and generation outputs limits. Two different techniques are used to solve the problem, namely dynamic programming and coordination equations. The mathematical algorithms for the solution of the problem were developed and the results of some computer studies on selected sample systems were obtained. The results show that using coordination equations method leads to lower total cumulative cost than using the dynamic programming method; however, many of the realistic system constraints cannot be properly handled by the coordination equations method.     

Hybrid Chemical Reaction Optimization Approach for Combined Economic Emission Short-term Hydrothermal Scheduling

—This article presents the hybridization of a newly developed, novel, and efficient chemical reaction optimization technique and differential evolution for solving a short-term hydrothermal scheduling problem. The main objective of the short-term scheduling is to schedule the hydro and thermal plants generation in such a way that minimizes the generation cost. However, due to strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained hydrothermal scheduling formulation is to estimate the optimal generation schedule of hydro and thermal generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. In this context, this article proposes a hybrid chemical reaction optimization and differential evolution approach for solving the multi-objective short-term combined economic emission scheduling problem. The effectiveness of the proposed hybrid chemical reaction optimization and differential evolution method is validated by carrying out extensive tests on two hydrothermal scheduling problems with incremental fuel-cost functions taking into account the valve-point loading effects. The result shows that the proposed algorithm improves the solution accuracy and reliability compared to other techniques.     

A distribution company energy acquisition market model with integration of distributed generation and load curtailment options

This work presents a novel day-ahead energy acquisition model for a distribution company (DisCo) in a competitive market based on Pool and financial bilateral contracts. The market structure encompasses wholesale generation companies, distributed generation (DG) units of independent producers, DG units owned by the DisCo, and load curtailment options. Thus, while satisfying its technical constraints, the DisCo purchases active and reactive power according to the offers of DG units, customers, and the wholesale market. The resulting optimal power flow model is implemented with an object-oriented approach, which is solved numerically by making use of a branch and border sequential quadratic programming algorithm. The model is validated in test systems and then applied to a real case study. Results show the general applicability of the proposed model, with potential cost savings for the DisCo. Finally, the analysis of Lagrange multipliers gives valuable information, which can be used to improve the market design and to extend the use of the model to a more general market structure such as a power exchange.     

考虑深度调峰的电力日前市场机制设计

中国电力系统缺乏灵活调峰资源,面临严重的弃风、弃光困局,迫切需要充分调动各种资源的调峰潜力。文中提出了考虑深度调峰的电力日前市场机制设计,将深度调峰、可再生能源消纳和日前市场相结合。设计了基于深度调峰报价和避免削减降价的市场机制,深度挖掘火电机组的调峰潜力,激励火电机组主动降低自身出力参与调峰,激励可再生能源出让部分发电上网的红利,从而以共赢的方式促进可再生能源的消纳。构建了考虑深度调峰的电力日前市场出清模型,实现火电与可再生能源在低谷时段的发电权交易,实现了火电深度调峰和日前市场出清的联合建模,解决了两个市场割裂的情况下难以考虑机组爬坡约束的问题。文中采用IEEE 118节点算例系统,验证了所提机制和模型的有效性。     

Power generation scheduling for multi-area hydro-thermal systemswith tie line constraints, cascaded reservoirs and uncertain data

The authors propose an approach to the short-term generation scheduling of hydro-thermal power systems (GSHT). The objective of GSHT is to minimize the total operation cost of thermal units over the scheduling time horizon. To solve the problem within a reasonable time, the problem is decomposed into thermal and hydro subproblems. The coordinator between these subproblems is the system Lagrange multiplier. For the thermal subproblem, in a multi-area power pool, it is necessary to coordinate the area generations for reducing the operation cost without violating tie limits. A probabilistic method is employed in considering load forecasting errors and forced outages of generating units to satisfy system reliability requirements. For the hydro subsystem, network flow concepts are adopted to coordinate water use over the entire study time span and the reduced gradient method is used to overcome the linear characteristic of the network flow method to obtain the optimal solution. Three case studies for the proposed method are presented     

Congestion management considering hydro-thermal combined operation in a pool based electricity market

This paper proposes a novel congestion management strategy for a pool based electricity market considering combined operation of hydro and thermal generator companies. The proposed congestion management problem is formulated as mixed binary nonlinear programming problem to minimize the cost of re-dispatching the hydro and thermal generator companies to alleviate congestion subject to operational, line overloading and water availability constraints. A piecewise-linearized unit performance curve is used in this formulation, which takes into account its non-concave nature. The effectiveness of the proposed technique is demonstrated by solving the modified IEEE 57-bus and IEEE 118-bus systems for congestion management under line outages.     

考虑日前小时电价的含风电电力系统调度模型研究简

为提高含大规模风电电力系统运行的灵活性,降低风电波动性对系统的影响和提高系统的风电接纳能力,在含风电电力系统调度中引入日前小时电价。基于考虑日前小时电价的风电最大接纳模式,建立了相应的数学模型和求解方法,以此为基础分析了引入日前小时电价模型在不同风电渗透率场景下对火电机组承担负荷、系统接纳风电能力和系统运行经济性的影响。算例分析表明,在含风电电力系统中引入日前小时电价能够缓解风电接入对电力系统的影响,提高系统的风电接纳能力与运行的经济性。     

考虑实时市场平衡费用的含风电日前市场电能-备用联合出清模型

风电出力的不确定性为日前市场出清带来了挑战。提出一种风电参与的日前电能-备用联合出清模型,采用多场景技术描述风电出力的不确定性,基于场景集合求解模型达到出清结果。所提出的模型引入风电出力不确定性所带来的备用购买费用和实时市场的平衡费用来考虑日前市场出清结果对实时市场的影响,以达到减少实时市场的平衡压力,保障系统稳定运行的效果。同时该模型考虑弃风和失负荷惩罚费用来保障风电资源的充分利用和系统的安全稳定运行。改进的IEEE30系统算例结果对比及分析表明,所提出的模型可充分利用风电资源,有效降低日前市场电能生产成本,并减少弃风量,降低失负荷风险。在风电大规模接入电力系统的情况下具有较好的实际应用价值。