Security-constrained Unit Commitment Research Articles

Coordination of Energy Storage and Wind Power Plant considering Energy and Reserve Market for a Resilience Smart Grid

Summary The intermittency of wind power generation causes some challenges in scheduling normal operation and emergency states. The presence of Pumped Storage (PS) power plants along with wind power plants can neutralize the negative impacts of varying production. It can also improve the resilience performance of the power system. The main components of this paper are to investigate the effect of coordination between wind and PS power plants and to consider the energy market and reserve on normal operation and emergency (resilience) of the power system. Since the output power of wind generation is intermittent, the scheduling of wind and PS is stochastic problem scheduling which is named stochastic Security-Constrained Unit Commitment (SCUC). The uncertainty of wind power generation and load are modeled by the Monte Carlo simulation method. Non-convexity and nonlinearity are two important points that prevent the global optimal solution to the problem. The proposed scheme is a Mixed-Integer Nonlinear Programming (MINLP) problem. The proposed model by Lagrange relaxation will become a Mixed-Integer Linear Programming (MILP) problem. One important factor that problem makes nonconvex is nonexistence affine state of equality constraints. Since AC load flow constraints make the problem a complex non-convex and non-linear problem. Because convexity and faster solving (the system should restore very quickly in an event.) is used DC load flow. Therefore, the optimal solution is guaranteed in this article. The obtained results demonstrate the effectiveness of the proposed method in normal and resiliency operation.

Fast identifying redundant security constraints in SCUC in the presence of uncertainties

Security constrained unit commitment (SCUC), with renewable resources integrated into power grid, is one core function in the day-ahead market. However, it confronts with critical challenges as the heavy complicated security constraints are considered. Usually, it is observed that lots of security constraints are redundant, which can be identified and eliminated, so as to reduce the computational complexity. In this study, the authors first lift the uncertain feasible region of SCUC into a high dimensional space. Furthermore, a fast identification method is proposed to relax the original feasible region, which thus can be solved by the classical greedy algorithm. In order to prevent the over-relaxation and find more redundant constraints, an efficient feasible-based bound tightening strategy is utilised to provide a tighter bound. Numerical results on large-scale test systems verify the effectiveness of the proposed method.

Optimal demand response strategies to mitigate wind power variability and gas‐supply uncertainty in a multi‐resolution robust security constrained unit commitment

With the increasing penetration rate of renewable energy sources, variability impacts, aside from uncertainty, will increase the need for operational flexibility resources in power systems. Accordingly, in this study, flexibility provision from both generation and demand sides are considered in a day-ahead scheduling problem. Fast start gas-fired units, due to their agility in starting-up and high ramping capabilities are considered as generation side flexibility providers. However, the normal operation of these units strictly depends on a secure gas supply through connected gas pipelines. Thus, gas-supply uncertainty is considered in the proposed model to mimic the impacts of deviations in gas volume through pipelines. On the other hand, demand response programs (DRPs) with their specific characteristics are among powerful demand side flexibility providers. However, unlike the existing literature that considers a supplementary role for DRPs, this study introduces the main role for DRPs as flexibility providers through comprehensive modelling of versatile DRPs. The proposed day-ahead scheduling problem is a multi-resolution robust security constrained unit commitment that the robustness comes from the decision making against sub-hourly gas-supply uncertainty and wind power variability. The applicability of the proposed model is tested on both an 8-bus and IEEE-118-bus standard test systems.

Hierarchical and Robust Scheduling Approach for VSC-MTDC Meshed AC/DC Grid With High Share of Wind Power

This paper proposes a novel hierarchical and robust scheduling paradigm for the emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid system with high share of wind power. Considering the multilevel structure of the VSC-MTDC meshed AC/DC system, the scheduling problem is decomposed into three interactive levels according to the hierarchical analytical target cascading (ATC) technique. The low level is a two-stage adaptive robust security-constrained unit commitment problem for the AC system, which is solved by the column-and-constraint generation (C&CG) algorithm; the middle level is a day-ahead power transmission optimization problem for the VSC stations, where a day-ahead operation model for the VSC station is presented to fully use its flexible and controllable power adjustment capability to promote wind power accommodation; and the high level is a multi-period optimal power flow problem for the DC grid. An integrated ATC and C&CG algorithm is proposed to enable the hierarchical and robust scheduling. The parallel solution is used to accelerate the hierarchical scheduling. Simulations show that this formulation is effective in dealing with wind power uncertainty, promoting wind power accommodation, and improving the calculation efficiency.

Stochastic two-stage reliability-based Security Constrained Unit Commitment in smart grid environment

In the realm of the smart grid, there are new issues that are needed to be addressed. However, these new concerns may have an adverse effect on the complexity of the operation of the network, they present great opportunities to the network operators for a better service, in terms of lower costs and enhanced security. In this paper, the integration of the coordinated aggregated Plug-in Electric Vehicle (PEV) fleets and Demand Response Programs (DRPs) into power systems is studied using the stochastic reliability-based Security Constrained Unit Commitment (SCUC). The proposed SCUC program minimizes network operation costs while determining the best strategy for deployment of PEVs and DRPs. Moreover, the proposed approach analyze the effect of these resources on the adequacy of the power system by considering reliability metrics Being a mobile power storing device enables the PEVs to provide their energy to the network when called upon. Moreover, DRPs can mitigate power consumption during the peak load or in case of a contingency. Optimal integration of these resources could reduce the network operating costs and improve security. A two-stage stochastic mixed-integer programming model is employed for power system modeling in a smart grid environment. In the proposed model, PEV fleets are modeled as virtual power plants that can provide electric power while DRPs are assumed to only affect the power consumption of the customers. The proposed paradigm is applied to the IEEE 6-bus and IEEE Reliability Test System (RTS) to illustrate its effectiveness. Numerical analyses confirm the effectiveness of the proposed strategy in lessening the cost of network operations, better performance in case of contingencies and a higher level of reliability.

Optimal scheduling of reconfigurable grids considering dynamic line rating constraint

Power system automation is an effective tool from both economical and technical aspects to improve the optimal operation of power generators. In this regard, Security Constrained Unit Commitment (SCUC) incorporating Dynamic Thermal Line Rating (DTLR) of overhead transmission lines can boost the system security effectively. Using Transmission Switching (TS) tool in SCUC problems leads to cost reduction. Still, one of the main challenges arisen in TS problems is the excessive number of switching in lines, which decreases the lifespan of power switches. In this paper, an Improved Linear AC Optimal Power Flow (ILACOPF) is proposed by using TS and considering Heat Balance Equation (HBE) as a security constraint. Merging dynamic thermal line rating (considering the weather conditions) in SCUC with TS, besides decreasing the number of switching and increasing the lifespan of power switches, causes a remarkable reduction in operating costs. In this power flow, system losses are modeled by linear formulations. Moreover, a linear approximation of the heat losses due to power flow through lines is proposed. To solve the proposed model, Benders' decomposition approach is applied. The performance of the proposed framework has been evaluated on 6-bus and 118-bus IEEE test systems.

Uncertainty Cost of Stochastic Producers: Metrics and Impacts on Power Grid Flexibility

The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques.

Analysis of the impact of sub-hourly unit commitment on power system dynamics

This paper discusses the impact of the sub-hourly unit commitment problem on power system dynamics. Such an impact is evaluated by means of a co-simulation platform that embeds a sub-hourly stochastic mixed-integer linear programming security constrained unit commitment (sSCUC) into a time domain simulator, as well as includes a rolling planning horizon that accounts for forecast updates. The paper considers different sub-hourly sSCUC resolutions (i.e., 5 and 15 min) and different wind penetration levels (i.e., 25 and 50%). The focus is on the transient response of the system and on frequency variations following different sSCUC strategies, and different sSCUC wind power uncertainty and volatility. The case study consists of a comprehensive set of Monte Carlo simulations based on the 39-bus system.

Unit Commitment Solution by Branch and Bound Algorithm

For a short period, to obtain a secured economic dispatch of power generation, Security Constrained Unit Commitment (SCUC) is programmed to commit the units by minimizing the operating costs while maintaining the balance between supply and demand in a deregulating environment. This paper discusses a vivid planning horizon of three and six periods distributed in a day taking into account the fixed cost, variable cost, start-up and shut-down cost, ramping up and down limits, minimum and maximum power bounds, spinning reserve constraints of the units and line load abilities which are all modelled to commit the generators in binary digits only. The proposed approach applies the Branch and Bound algorithm to test the system. The iterative process will converge, if the optimal solution exists. In this paper three and six bus systems are analyzed to prove the effectiveness of this model which can be extended for larger systems too.

A Cost Effective Solution to Security Constrained Unit Commitment and Dispatch Problem using Hybrid Search Algorithm

The Security constraints unit commitment (SCUC) is a powerful scheduling technique used in power markets for daily planning. SCUC is a framework that combines two common algorithms in the electricity industry: Unit Commitment (UC) and Economic Dispatch (ED), while adding a new dimension – Security. Load demand is changing continuously due to variation of load of industrial, residential and commercial sectors. Thus, it is very important to decide which generating unit should be kept on and how much power should be dispatched so that time varying load demand can be meet on Hourly basis and there should be no scope of occurrence of loss of load hour (LOLH). In the proposed research, the recently developed hybrid meta-heuristics search algorithm i.e. GWO-RES has been applied to solve the security constrained Unit Commitment and dispatch problem of Electric power system. The efficiency of the proposed hybrid algorithm has been tested for standard IEEE-14 Bus, 30-Bus and 56-bus system and it has been experimentally found that GWO-RES performs much better than hybrid GWO-PS and GWO-RS algorithm.

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.

Heuristic bender decomposition efficiency improvement for SCUC

The Benders Decomposition method is widely used to manage large-scale problems in the power system optimization. In this letter, a simple but effective method is proposed to improve the Benders Decomposition efficiency with the security constrained unit commitment (SCUC) problem taken as an example. The heuristic weights are introduced for constraint violations to accelerate the elimination of them. Thus the Benders Decomposition efficiency can be improved. The validity of the proposed method has been verified by case studies on multiple IEEE test systems.

Stochastically coordinated transmission and distribution system operation with large-scale wind farms

With the high penetration of renewable energies in modern power systems, deterministic coordination algorithms are facing two major problems: one is degradation in accuracy if fewer scenarios are utilized for uncertainty evaluation while second is the high computational time if a high number of scenarios are considered for better accuracy. In both cases, the efficiency of the algorithm is degraded. To solve these problems in coupled transmission system and distribution systems (TSDS), probabilistic coordination algorithms are adopted to solve with less effort. In this paper, a TSDS probabilistic coordination model is proposed to solve the coordinated security-constrained unit commitment problem. A mean and standard deviation matching based probabilistic analytical target cascading algorithm has been utilized for evaluation of TSDS coordination problem. Instead of solving each scenario as a separate problem, the proposed algorithm considers a single coordination problem with probabilistic characteristics as shared variables and hence, achieves fast convergence. Different case studies are performed to prove the efficacy of the proposed algorithm. Results verify that the proposed algorithm reduces computational time and resources for large-scale systems.

Effects of Local Electricity Trading on Power Flows and Voltage Levels for Different Elasticities and Prices

Local electricity trading is a concept that allows active electricity trading between consumers, producers and/or prosumers located in a local low voltage distribution grid. The concept should provide added value to the participants and accelerate the democratization, decarbonization and decentralization of the power sector. The effects of local electricity trading on voltage levels in distribution grids are just in the early stage of research, together with the possible means of control, market design, market-clearing approaches and integration of the local electricity trading within the electricity markets. The aim of this work is to contribute to the research by examining if near real-time local electricity trading can be implemented in a distribution grid without time-consuming security-constrained unit commitment calculations for the observed time horizon and without security-constrained economic dispatch calculations for each trading period. Moreover, this work investigates if the implementation of local electricity trading can contribute to the avoidance of unpredictable and unfavorable consumption/production patterns, which can appear in the distribution grid due to the random behavior of a large number of participants. It is analyzed if a contribution to the maintenance of the voltages and currents within limits can be achieved that way. The method for simulation of a local electricity market and analysis of power flows and voltage levels is presented. The auction-based local electricity trading is simulated and applied on the modified IEEE European Low Voltage Test Feeder where the effects of local electricity trading on power flows and voltage levels are studied for boundary elasticities and prices of demand and supply offering curves. It is shown that the local electricity trading has potential to incentivize active participation of prosumers, which can lead to better demand/supply balancing at the local level and to a decrease of voltage fluctuations.

Security-Constrained Unit Commitment Problem With Transmission Switching Reliability and Dynamic Thermal Line Rating

In security-constrained unit commitment (SCUC) problems, one approach to decrease operation costs is using a transmission switching (TS) tool. In SCUC problems with TS, one of the main challenges is that there is no limitation for the number of switching of circuit breakers (CB) in the system. In this article, the reliability of CB is merged into the SCUC problem with the TS and is considered as a limiting factor for switching. With a more reliable CB, the overall reliability of the system will be increased. So, it can be concluded that the reliability of a CB affects the amount of load shedding. Reliability of a CB is a nonlinear equation based on the number of switching in a period. An approach is presented to linearize the switch reliability equation. In this article, the power flow model uses an improved linear ac optimal power flow and a dynamic thermal line rating (DTLR) model, which considers the weather conditions. Other than CB reliability, DTLR in SCUC problems affects the number of switching and, as a result, operation costs will be significantly decreased. The proposed model is empowered by Bender's decomposition and is tested on 6-bus and 118-bus IEEE test systems.

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

An Improved Constrained Order Optimization Algorithm for Uncertain SCUC Problem Solving

Studying the faster and more efficient method of solving the uncertain security-constrained unit commitment (SCUC) problem is an urgent need for the development of power systems under the background of large-scale wind power access and power dispatching. This study proposes an improved constrained order optimization (COO) algorithm to solve the uncertain SCUC problem. First, the data-driven discrete variable identification strategy is incorporated into the COO rough model, and then, the invalid security constraints identification strategy is incorporated into the COO accurate model. Finally, the improved COO algorithm combines the discrete variable identification with the invalid security constraint identification to make the uncertain SCUC decision. The results of the IEEE 118-bus test system showed that, compared with the traditional COO algorithm, the improved COO algorithm proposed has higher accuracy and better efficiency.

An Integrated Preventive Operation Framework for Power Systems During Hurricanes

Severe weather is the primary cause of power outages in the U.S. Despite the availability of weather forecast information, such data are not systematically integrated into operation models. This article proposes an integrated framework to convert weather forecast into appropriate information for preventive operation during hurricanes so that the power outages induced by hurricanes can be reduced. To achieve this goal, first, a structural model of the transmission towers is developed to estimate failure probabilities based on the wind speed. These probabilities are then integrated within a day-ahead security-constrained unit commitment framework to guide preventive operation. The resulting day-ahead schedule will be more reliable as it will rely less on the elements that are likely to fail. Simulation studies, conducted on the IEEE 118-bus system affected by synthesized Irma and Harvey hurricanes, showed that the proposed framework was able to prevent 33% to 83% of the blackouts. Further research is required to investigate the impacts of flooding, damage to the distribution network, and weather forecast uncertainty.

Optimization of the integration among traditional fossil fuels, clean energies, renewable sources, and energy storages: An MILP model for the coupled electric power, hydraulic, and natural gas systems

This paper presents a Mixed Integer Linear Programming (MILP) formulation to optimize the Security Constrained Unit Commitment problem, integrating three areas: electrical, hydraulic, and natural gas. The model considers thermal generation from natural gas units and other fossil fuel units, hydraulic generation, pumped storage units, hydraulic head effects for the generation and pumping modes, power flow transmission constraints, natural gas supply and transportation, gas flows in single pipelines and pipelines with compressors, and line-pack for gas pipelines. The original model is hard to solve due to nonconvexities and nonlinearities of several constraints, but with the application of correct linearization techniques, accurate MILP models can be efficiently obtained and solved. The proposed MILP model differs from most of other approaches available in the literature in that it applies to several types of power generation, and includes a large number of real characteristics of the integrated system.A modified IEEE 31-bus with 2 pumped stations and 7-node natural gas system is presented to illustrate the effectiveness of the proposed model; test cases are solved by commercial package Gurobi. Test results indicate that the proposed approach provides feasible solutions with convenient CPU times. The obtained results are discussed and analyzed through simple graphs.

Electric Vehicle Battery Swapping-Charging System in Power Generation Scheduling for Managing Ambient Air Quality and Human Health Conditions

The proliferation of electric vehicles (EVs) offers additional opportunities for improving the ambient air quality and reducing the associated health impacts. However, such opportunities could become unattainable if the electricity for charging EVs is mainly supplied by coal-fired units. This paper aims to enhance the environmental benefits of EVs by differentiating the marginal impacts of coal-fired unit emissions at various times and locations on ambient air pollutant concentration (AAPC) and human health conditions. The proposed cost-effective approach will assign EV battery charging loads and other electricity demands to generation units with low health impacts. The proposed air pollution dispersion model maps the emission into spatial AAPC increments ( ${\Delta }$ AAPCs), and concentration-response function maps ${\Delta }$ AAPCs into health impacts. As such, we estimate differentiating health impacts of emissions at various times and locations without executing large-scale atmospheric models. We also integrate differentiating emission regulations with the joint optimization of security-constrained unit commitment (SCUC) and EV battery swapping-charging system (BSCS). BSCS refers to the optimal scheduling of an aggregated number of EV batteries which are centrally charged and then dispatched via delivery trucks to battery swapping stations (BSSs) to supply local EVs. BSCS determines optimal schedules for battery charging, swapping, and truck routing. A Lagrangian decomposition method decouples the formulated large-scale mixed-integer linear programming model into SCUC and BSCS subproblems. The case studies demonstrate the effectiveness of the proposed approach for managing ambient air quality and human health conditions.