Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

Renewable energy sources(RES)such as wind turbines(WT)and solar cells have attracted the attention of power system operators and users alike,thanks to their lack of environmental pollution,independence of fossil fuels,and meager marginal costs.With the introduction of RES,challenges have faced the unit commitment(UC)problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’inputs and outputs,and specifying the optimal generation of each unit.The output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with uncertainty.As a result,the UC problem in the presence of RES faces uncertainties.The grid consumed load is not always equal to and is randomly different from the predicted values,which also contributes to uncertainty in solving the aforementioned problem.The current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this problem.The new model is adopted to solve the wind-generated power uncertainty,and energy storage systems(ESSs)are included in the problem for further management.The problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage units.To solve the UC programming model,a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.

Integration of power-to-hydrogen in day-ahead security-constrained unit commitment with high wind penetration

The increasing integration of variable wind generation has aggravated the imbalance between electricity supply and demand. Power-to-hydrogen(P2H) is a promising solution to balance supply and demand in a variable power grid, in which excess wind power is converted into hydrogen via electrolysis and stored for later use. In this study, an energy hub(EH) with both a P2H facility(electrolyzer) and a gas-to-power(G2P) facility(hydrogen gas turbine) is proposed to accommodate a high penetration of wind power. The EH is modeled and integrated into a security-constrained unit commitment(SCUC) problem, and this optimization problem is solved by a mixed-integer linear programming(MILP) method with the Benders decomposition technique. Case studies are presented to validate the proposed model and elaborate on the technological potential of integrating P2H into a power system with a high level of wind penetration(HWP).

A Comprehensive Review of Security-constrained Unit Commitment

Security-constrained unit commitment(SCUC)has been extensively studied as a key decision-making tool to determine optimal power generation schedules in the operation of electricity market.With the development of emerging power grids,fruitful research results on SCUC have been obtained.Therefore,it is essential to review current work and propose future directions for SCUC to meet the needs of developing power systems.In this paper,the basic mathematical model of the standard SCUC is summarized,and the characteristics and application scopes of common solution algorithms are presented.Customized models focusing on diverse mathematical properties are then categorized and the corresponding solving methodologies are discussed.Finally,research trends in the field are prospected based on a summary of the state-of-the-art and latest studies.It is hoped that this paper can be a useful reference to support theoretical research and practical applications of SCUC in the future.

A Fast Calculation Method for Long-term Security-constrained Unit Commitment of Large-scale Power Systems with Renewable Energy

With the increase in the penetration rate of renewable energy, the planning and operation of power systems will face huge challenges. To ensure the sufficient utilization of renewable energy, the reasonable arrangement for the long-term power generation plan has become more crucial. Security-constrained unit commitment(SCUC) is a critical technical means to optimize the long-term power generation plan. However, the plentiful power sources and the complex grid structure in largescale power systems will bring great difficulties to long-term SCUC. In this paper, we propose a fast calculation method for long-term SCUC of large-scale power systems with renewable energy. First, a method for unit status reduction based on temporal decomposition is proposed, which will reduce plenty of binary variables and intertemporal constraints in SCUC. Then,an efficient redundant constraint identification(RCI) method is developed to reduce the number of network constraints. Furthermore, a joint accelerated calculation framework for status reduction and RCI is formed, which can reduce the complexity of long-term SCUC while ensuring a high-precision feasible solution. In case studies, numerical results based on two test systems ROTS2017 and NREL-118 are analyzed, which verify the effectiveness and scalability of the proposed calculation method.

Graph Theory Based N-1 Transmission Contingency Selection and Its Application in Security-constrained Unit Commitment

Contingency analysis is an important building block in the stability and reliability analysis of power grid operations. However, due to the large number of transmission lines,in practice only a limited number of contingencies could be evaluated. This paper proposes a graph theory based N-1 contingency selection method to effectively identify the most critical contingencies, which can be used in security-constrained unit commitment(SCUC) problems to derive secure and economic operation decisions of the power grid. Specifically, the power flow transferring path identification algorithm and the vulnerability index are put forward to rank individual contingencies according to potential transmission line overloads. Effectiveness of the proposed N-1 contingency selection method is quantified by applying the corresponding SCUC solution to the full N-1 security evaluation, i. e., quantifying total post-contingency generation-load imbalance in all N-1 contingencies. Numerical results on several benchmark IEEE systems, including5-bus, 24-bus, and 118-bus systems, show effectiveness of the proposed method. Compared with existing contingency selection methods which usually resort to full power flow calculations,the proposed method relies on power gird topology to effectively identify critical contingencies for facilitating the optimal scheduling of SCUC problems.

Security-constrained Transmission Maintenance Optimization Considering Generation and Operational Risk Costs

With the large-scale integration of renewable energy,the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy generation.Simultaneously,in a market-oriented operation mode,the power dispatching control center aims to reduce the overall power purchase cost while ensuring the security of the power system.Therefore,a security-constrained transmission maintenance optimization model considering generation and operational risk costs is proposed herein.This model is built on double-layer optimization framework,where the upper-layer model is used for maintenance and generation planning,and the lowerlayer model is primarily used to address the operational security risk arising from the random prediction error and N-1 transmission failure.Correspondingly,a generation-maintenance iterative algorithm based on a defined cost feedback is included to increase solution efficiency.Generation cost is determined using long-term security-constrained unit commitment,and the operational risk cost is obtained using a double-layer N-1 risk assessment model.An electrical correlation coupling coefficient is proposed for the solution process to avoid maintenance of associated equipment simultaneously,thereby improving model convergence efficiency.The IEEE 118-bus system is used as a test case for illustration,and test results suggest that the proposed model and algorithm can reduce the total cost of transmission maintenance and system operation while effectively improving the solution efficiency of the joint optimization model.

Security Constrained Unit Commitment with Extreme Wind Scenarios

The rapid development of economy and society stimulates the increase of power demand. Wind power has received great attention as a typical renewable energy, and the share of wind power is continually increasing in recent years.However, the high integration of wind power brings challenges to the secure and reliable operation of power grid due to the intermittent characteristic of wind power. In order to solve the operation risk caused by wind power uncertainty, this paper proposes to solve the problem of stochastic security-constrained unit commitment(SCUC) by considering the extreme scenarios of wind power output. Firstly, assuming that the probability density distribution of wind power approximately follows a normal distribution, a great number of scenarios are generated by Monte Carlo(MC) simulation method to capture the stochastic nature of wind power output. Then, the clustering by fast search and find of density peaks(CSFDP) is utilized to separate the generated scenarios into three types: extreme, normal and typical scenarios. The extreme scenarios are identified to determine the on/off statuses of generators, while the typical scenarios are used to solve the day-ahead security-constrained economic dispatch(SCED) problem. The advantage of the proposed method is to ensure the robustness of SCUC solution while reducing the conservativeness of the solution as much as possible.The effectiveness of the proposed method is verified by IEEE test systems.

Security-constrained model for integrated power and natural-gas system

One of the main factors impacting the reliability of energy systems nowadays is the growing interdependence between electricity and gas networks due to the increase in the installation of gas-fired units. Securityconstrained unit commitment(SCUC) models are used to economically schedule generating units without compromising the system reliability. This paper proposes a novel SCUC formulation that includes dynamic gas constraints,such as the line pack, and transmission contingencies in power and gas networks for studying the integrated system reliability. A Benders’ decomposition with linear programming techniques is developed to be able to study large systems. By including dynamic gas constraints into the SCUC, the proposed model accounts for the flexibility and reliability that power systems require from gas systems in the short term. Case studies of different size and complexity are employed to illustrate how the reliability of one system is affected by the reliability of the other. These experiments show how both systems operate in a secure way(by including contingencies) increases operating costs by approximately 9% and also show how these costs can vary by 24% depending on the line pack scheduling.

Role of optimal transmission switching in accommodating renewable energy in deep peak regulation-enabled power systems

Due to the shortage of fossil energy and the pollution caused by combustion of fossil fuels,the proportion of renewable energy in power systems is gradually increasing across the world.Accordingly,the capacity of power systems to accommodate renewable energy must be improved.However,integration of a large amount of renewable energy into power grids may result in network congestion.Hence,in this study,optimal transmission switching(OTS)is considered as an important method of accommodating renewable energy.It is incorporated into the operation of a power grid along with deep peak regulation of thermal power units,forming an interactive mode of coordinated operation of source and network.A stochastic unit commitment model consider!ng deep peak regulation and OTS is established,and the role of OTS in promoting the accommodation of renewable energy is analyzed quantitatively.The results of case studies involving the IEEE 30-bus system demonstrate that OTS can enable utilization of the potential of deep peak regulation and facilitate the accommodation of renewable energy.

Big-M based MILP method for SCUC considering allowable wind power output interval and its adjustable conservativeness

In contrast to most existing works on robust unit commitment(UC),this study proposes a novel big-M-based mixed-integer linear programming(MILP)method to solve security-constrained UC problems considering the allowable wind power output interval and its adjustable conservativeness.The wind power accommodation capability is usually limited by spinning reserve requirements and transmission line capacity in power systems with large-scale wind power integration.Therefore,by employing the big-M method and adding auxiliary 0-1 binary variables to describe the allowable wind power output interval,a bilinear programming problem meeting the security constraints of system operation is presented.Furthermore,an adjustable confidence level was introduced into the proposed robust optimization model to decrease the level of conservatism of the robust solutions.This can establish a trade-off between economy and security.To develop an MILP problem that can be solved by commercial solvers such as CPLEX,the big-M method is utilized again to represent the bilinear formulation as a series of linear inequality constraints and approximately address the nonlinear formulation caused by the adjustable conservativeness.Simulation studies on a modified IEEE 26-generator reliability test system connected to wind farms were performed to confirm the effectiveness and advantages of the proposed method.

基于预期完成率的月内滚动机组组合研究

为保障月度交易计划执行,提出了一种基于预期完成率的月内滚动机组组合方法,实现机组开停方式、电量计划完成情况和后续供需形势之间的有效匹配。为避免发电机组开停方式频繁调整,定义了发电厂预期完成率评价指标,以量化原发电机组组合方式下电量计划执行风险。若执行风险超过预期,则启动月内滚动机组组合。月内滚动机组组合以保障月度交易计划执行且各发电厂完成率均衡为优化目标,综合考虑电力电量平衡、网络传输能力、机组出力范围等约束条件,能够对当月后续运行日的发电机组组合方式优化调整。最后,基于IEEE-30节点系统构造的算例表明,预期完成率指标能够避免发电机组组合方式的频繁调整,保障发电厂月度交易计划可靠执行。