Security-constrained Unit Commitment Model Research Articles

Security-constrained unit commitment model considering frequency and voltage stabilities with multiresource participation

The rapidly increasing proportion of renewable energy sources has led to a reduction in the relative share of synchronous units, which has resulted in a decline in the inertia of the power system and a decrease in its voltage support capacity. This has led to several issues related to the frequency and voltage stabilities of the power system. To ensure these frequency and voltage stabilities, it is necessary to maintain a number of synchronous units online in the day-ahead generation schedule. First, the dynamic frequency change process after a power system fault is discussed, and a linear expression is derived for the frequency stability constraints involving energy storage systems and wind turbines. Second, the short-circuit capacity of the bus is characterized to describe the strength of voltage support, and the minimum short-circuit capacity requirement of the bus is solved based on the transient voltage recovery problem after clearance of the short-circuit faults. The total short-circuit capacity provided by the unit to the bus is then calculated through the network reactance matrix to establish the voltage stability constraint. Subsequently, the security-constrained unit commitment model considering frequency and voltage stabilities (FVS-SCUC) is established. Finally, the effectiveness of the proposed model is demonstrated through a numerical simulation of the IEEE-39 system comprising storage and wind turbines. The model ensures the frequency and voltage securities of the system, and the renewable energy output is improved upon considering energy storage. Thus, the overall system cost was reduced by nearly 30% by considering the frequency regulation effects of the energy storage system and wind turbine as well as the voltage regulation effects of the energy storage system.

Frequency security-constrained unit commitment with fast frequency support of DFIG-based wind power plants

The integration of inverter-based renewable energy sources into power grids presents a unique challenge: a reduction in grid inertia, leading to potential frequency stability issues. This paper introduces an innovative approach to address this challenge, focusing on the dynamic role of wind power plants equipped with dual-fed induction generators (DFIG). Unlike traditional methods that rely on fixed inertia control strategies and wind power droop coefficients, our method offers a dynamic solution tailored to the fluctuating nature of grid frequency dynamics. We propose a novel variable inertia support mechanism, coupled with an optimized power point tracking control and strategic reserve, to maximize the effective use of wind power reserves without waste. Additionally, we account for the inherent uncertainties in wind power generation, enhancing the frequency support capabilities of wind turbines through a fast frequency response strategy. A key feature of our approach is the application of an advanced piecewise linearization fitting method to establish a robust frequency nadir constraint. This is integrated into a two-stage robust frequency security-constrained unit commitment model, enabling optimal real-time decision making for frequency stability in wind power plants. The efficacy and practicality of our proposed method are verified through a detailed case study on a modified IEEE 39-bus system and a hardware-in-the-loop experiment. Compared with the traditional fixed parameter control method, the cost is reduced by approximately 0.42%, while the linearization error at frequency nadirs remains within the range of -4% to 1.5%.

Ensemble Provably Robust Learn-to-Optimize Approach for Security-Constrained Unit Commitment

Security-constrained unit commitment (SCUC) is the basis for power systems and markets operation, which is solved periodically via mixed-integer programming (MIP) with limited input data changes to historical solved instances. This paper proposes an ensemble provably robust learn-to-optimize approach (EPR-L2O) for transforming the MIP-based SCUC into the tractable convex problem with predictable solving time by identifying the integer part of MIP, denoted by unit status integer solution. It formulates the SCUC model for unit status integer solution generation, prunes the generated integer solutions, clusters the units based on their operation status, and proposes the provably robust learning approach to training the unit status integer solution identifiers, which are provably robust against the input adversarial perturbation. After learning, the integer solutions from different identifiers are assembled to identify the integer variables of SCUC. Case study verifies that: 1) EPR-L2O achieves 6.91, 19.71, and 22.14 times acceleration in the 30-bus, 118-bus, and 300-bus systems than the Branch-and-Bound; 2) compared to cross-entropy-based learning, EPR-L2O reduces the MIP gap by 0.031%, 0.21%, and 0.0084% in the 30-bus, 118-bus, and 300-bus systems, indicating its operation economy and robustness.

A two-stage SCUC model for distribution networks considering uncertainty and demand response

Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.

Proactive Security-Constrained Unit Commitment Against Typhoon Disasters: An Approximate Dynamic Programming Approach

Proactive security-constrained unit commitment (SCUC) is one of the effective resilience-oriented responses to keep the secure operation of the power system during natural disasters. The approaches proposed in the previous research works cannot guarantee high-quality response timely to various emergencies during typhoon disasters. This paper proposes a proactive SCUC model solved by an approximate dynamic programming (ADP) algorithm to improve system resilience during typhoon disasters while meeting the requirements of the computational time. Both the uncertainty of the typhoon motion path and the wind speed-dependent transmission line failure probability are considered in the scenario generation. The optimization process is formulated as a Markov decision process (MDP), and a piecewise linear function (PLF) based ADP is employed for solving the model. With the exogenous information extracted from generated scenarios and recorded in the slopes of PLFs, the ADP algorithm can result in a near-optimal solution within a short computational time. Simulations are carried out using the modified IEEE RTS-79 system. The significant computational time saving and high-quality solution demonstrate the effectiveness of the proposed approach.

A Solution Method for Many-Objective Security-Constrained Unit Commitment Considering Flexibility

With the increasing penetration of wind power into power systems and the random fluctuation of the wind farm (WF) output, system flexibility must be considered in the optimal generation dispatch. Based on the extreme scenarios of the WF output, we proposed a flexibility risk index to evaluate the system flexibility of each time interval. We established a five-objective security-constrained unit commitment (SCUC) model of a power system with WFs, thermal generation plants, battery energy storage stations, and pumped storage hydro stations. The objectives were to minimize the system flexibility risk, total network loss, operation cost, power purchase cost, and pollutant gas emissions. To obtain the Pareto optimal solutions of the model, based on the objective selection and ε-constraint methods, we proposed two methods to reduce the dimension of objectives and transformed the five-objective optimization model into a series of three-objective optimization models. Then, we used the normalized normal constraint method to solve the Pareto frontier surface of each three-objective optimization model. We used a color column to represent the value change of the two upper layer objectives and visualized the Pareto frontier of the five-objective SCUC model in the three-dimensional ordinate space. Case studies on the modified IEEE-9 bus system and an actual power grid demonstrated the effectiveness and high computational efficiency of the proposed method.

Decentralized robust unit commitment for AC/DC interconnected power systems considering wind power uncertainty

The growing penetration of wind energy and the increasing AC/DC interconnections of regional power grids critically need to coordinate the multi-area unit commitment and reserve schedules. This paper proposes a decentralized robust optimization framework for the unit commitment and reserve allocation into bulk AC/DC interconnected systems. In the proposed framework, the scheduling problem is decomposed and handled by the analytical target cascading (ATC) technique. The upper-level master problem coordinates and updates the transmission plan of the high-voltage direct current (HVDC) tie-line. The lower-level regional sub-problems are formulated as robust security-constrained unit commitment models to co-optimize the generation and flexible reserve scheme in a parallel manner. The regional sub-problems precisely model the time-coupled flexible reserve supply of multiple flexible resources to cope with the wind power uncertainty and are solved by the column-and-constraint generation algorithm. Numerical simulations are conducted and comparative analysis is performed to validate the effectiveness and feasibility of the proposed framework.

An Accelerated-Decomposition Approach for Security-Constrained Unit Commitment With Corrective Network Reconfiguration

Security-constrained unit commitment (SCUC) model is used for power system day-ahead scheduling. However, current SCUC model uses a static network to deliver power and meet demand optimally. A dynamic network can provide a lower optimal cost and alleviate network congestion. However, due to the computational complexity and the lack of effective algorithms, network reconfiguration has not been included in the SCUC model yet. This paper presents a novel approach to handle the computational complexity in security-constrained unit commitment (SCUC) with corrective network reconfiguration (CNR) while considering the scalability through accelerated-decomposition approach with fast screening non-critical sub-problems of SCUC-CNR. The proposed approach provides substantial computational benefits and is also applicable to SCUC. Simulation results on the IEEE 24-bus system show that the proposed methods are substantially faster without the loss in solution quality while the scalability benefits are demonstrated using larger cases: the IEEE 73-bus system, IEEE 118-bus system and Polish system.

Electricity-heat integrated agent modelling for participation in spot electricity market

Due to the limitation of market rules, many small capacity power plants cannot directly participate in the spot electricity market, reducing the ability of the market to optimize the allocation of power generation resources. Among these power plants, the electric power output of some combined heat and power (CHP) plants is constrained by their heat output, which should be considered in the dispatch. Therefore, the electricity-heat integrated agents are defined and introduced to aggregate small capacity power plants and participate in the spot electricity market. Firstly, the model of electricity-heat integrated agents is formulated based on the aggregation of the internal generators and CHP units. Then, the framework of the spot electricity market with the participation of electricity-heat integrated agents is constructed. Moreover, the market clearing model is further developed by combining the electricity-heat integrated agent model and the security constrained unit commitment model. Simulation results in the case verify the effectiveness of the proposed model and technique.

Robust Pricing of Energy and Ancillary Services in Combined Electricity and Natural Gas Markets

Rapid growth of gas-fired generators strengthens the interdependency of electricity and natural gas markets. To meet the uncertainty challenge, a robust day-ahead market clearing framework for the integrated electricity and natural gas system (IEGS) is presented. First, a robust security-constrained unit commitment (SCUC) model is built considering the joint dispatch of ancillary services. Regulation-up/down reserve is scheduled to balance electrical load/wind power uncertainties, spinning reserve is to manage N-1 generator outage, and natural gas reserve is to withstand gas load uncertainty and provide fuel for gas-fired electrical reserve. The SCUC model is a two-stage mixed-integer nonlinear optimization problem so that a sequential linear approximation-based Column & Constraint Generation (SLA-based C&CG) algorithm is proposed. Then, according to SCUC solutions, an economic dispatch model is derived to obtain market clearing and pricing results. Based on locational marginal pricing, novel definitions of electrical energy, natural gas and multiple ancillary service prices are proposed, which can reflect network limits and uncertainty effects. Numerical cases on two test IEGSs and a provincial-level IEGS demonstrate the high accuracy of proposed SLA-based C&CG algorithm and the impact of uncertainties on market clearing.

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve si-multaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is noncon-vex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

Security-Constrained Unit Commitment for Hybrid VSC-MTDC/AC Power Systems With High Penetration of Wind Generation

This paper presents the solution to the security-constrained unit commitment (SCUC) problem for hybrid voltage source converter (VSC) based multi-terminal DC/AC (VSC-MTDC/AC) power systems with high penetration of wind generation. The formulated SCUC model presents a detailed representation of the VSCs with two different VSC control strategies considered, constant DC voltage control (master-slave control) and DC voltage droop control. In addition, Grid Code for wind farm connection is considered. Benders decomposition is used to solve the formulated SCUC problem by decomposing it into a master problem for solving unit commitment (UC) and hourly transmission security check subproblems. The final SCUC solution provides an economic and secure operation and control strategy for the meshed MTDC/AC system. Numerical tests illustrated the efficiency of the proposed SCUC model.

Congestion-aware robust security constrained unit commitment model for AC-DC grids

Due to the increasing penetration of wind energy, their natural volatility may cause power flow congestions by blocking some key transmission interfaces. To further reduce energy violations induced by uncertainty issues and ensure the system reliability, the controllability of High Voltage Direct Current (HVDC) system should be exploited in AC-DC grids. By leveraging the congestion management capabilities of HVDC, we propose a congestion-aware robust security constrained unit commitment (SCUC) model, to preemptively consider the plausible congestion circumstances in a more adaptive way in the day-ahead (DA) market. In our model, the conventional uncertainty budget sets are modified by the proposed congestion-aware factors, where the typical operational characteristics of traditional corrective actions and HVDC regulations in real-time (RT) market are formulated adaptively. Moreover, the feasibility region of proposed problem is further pruned by the optimality cut planes where more pertinent real-time information are included, thus the local optimal solution is eliminated and the superior economic benefits are obtained in our model. To solve this mix-integer linear problem (MILP), the model is divided into three parts including the DA decision model, the traditional corrective dispatch model, and the HVDC regulation dispatch model. Correspondingly, an improved three-level Benders Decomposition algorithm is developed to solve the model. The enhanced performance of the proposed model was validated in a modified IEEE 118-bus test case. The results show that, in our model, the flexible regulation of HVDC system optimally allocate the operational burdens between DA and RT markets, thus the congestion-aware robust SCUC model further captures the economic benefits when ensuring the model’s robustness.

Analysis of power market simulation methods under the background of re-electrification

The world is experiencing a new round energy transition from fossil energy to clean energy. To many countries, continuously upgrading electrification level and accommodate more wind power and solar power to achieve re-electrification roadmap, will be a trend and the only way of these countries’ energy transition. In this paper, challenges brought by large-scale wind power and solar power to power system operation are analyzed from the aspects of accommodation, fluctuation characteristics, prediction error, power electronics characteristics and pulling down system marginal cost. Also, key issues of power market simulation methods, such as co-considered day-ahead market and real-time market by use of Security Constrained Unit Commitment (SCUC) model and Security Constrained Economic Dispatch (SCED) model respectively, co-optimized energy and ancillary service market and added rotational inertia verification in SCUC model, are studied.

Unit Commitment with Tunable Frequency Response from Wind Turbine Generators

Based on the natural inertia on the rotors, wind turbine generators can provide both inertia response and primary frequency response to support the system frequency stability. In this paper, by considering the coordinated optimization of frequency response, a unit commitment with tunable frequency response from wind turbine generators is proposed. The relationship between inertia response and primary frequency response is analyzed. A frequency response correlation constraint is proposed to eliminate the second frequency nadir. Considering the system frequency stability constraints and the feasible region of frequency response provided by wind turbine generators, a security-constrained unit commitment model with tunable frequency response is established. According to the actual operation mode of the power system, a bi-level algorithm is adopted to calculate the frequency response parameters of wind turbine generators. The simulation result of the GB 2030 system demonstrates that the proposed method can reduce operating costs while ensuring system stability.

Research on the impact of demand response programs in day ahead market on the oligarchs in the electricity market

In order to study the influence of different types of demand response on the members of the electricity market in the market, a multi-agent model is used to study the behavioral strategies of independent market oligarchs in the electricity market in the day-ahead market, and the security-constrained unit commitment model is used to simulate specific market transactions. First, a demand response model was established, and the relationship between consumer profit and cost was determined using Taylor series expansion. Then the characteristics of the oligopoly market were analyzed, and a market clearing model by using a commitment model of security-constrained units was established, with the objective of maximizing agency profit, and including operation constraints. Finally, the IEEE6 node system was used for simulation to analyze the influence of different types of demand response behaviors on oligarchs and demonstrate the effectiveness of the model.

An Improved Robust SCUC Approach Considering Multiple Uncertainty and Correlation

With the large‐scale integration of wind power and photovoltaic power, as well as the rapid development of power systems in recent years, it is of great significance to study meticulous day‐ahead scheduling methods further. A robust security‐constrained unit commitment (SCUC) approach with the considerations of uncertainty outputs and the corresponding probability correlations is proposed in this paper. First, an improved robust SCUC model is constructed according to the multiple uncertainties of outputs and their probability correlations. Then, Cholesky decomposition is used to convert the correlated random samples into independent ones so as to determine the worst scenario directly based on the sample characteristics. Finally, the model is solved by Benders decomposition method. The simulation results based on the IEEE 118‐bus system indicate that the proposed approach can effectively improve the economy while ensuring the robustness of the day‐ahead SCUC decisions under multiple uncertainties. Additionally, the worst scenario determination method based on Cholesky decomposition can effectively promote the compactness of the robust SCUC model and significantly improve the computational efficiency accordingly. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Techno-economic and environmental assessment of the coordinated operation of regional grid-connected energy hubs considering high penetration of wind power

Nowadays, the high penetration of renewable energy sources (RESs) with non-uniformly distributed patterns has created unprecedented challenges for regional power systems to maintain system flexibility and reliability. These technical challenges obligate power system operators to curtail part of the produced renewable energy at various scheduling intervals. Motivated by these challenges, grid-connected energy hubs are seen as a way forward to boost system flexibility, decrease the rate of renewable power curtailment, and increase energy efficiency. However, simplified models may significantly affect the performance of the grid-connected energy hubs in practice. Hence, this paper proposes a holistic structure to determine the optimal coordinated operation of the grid-connected energy hubs and the regional power system by relying on the high penetration of wind power. In this regard, various fundamental challenges that have not yet been addressed in an integrated manner, including the CO2 emission rate and the amount of curtailed renewable energy along with total operating costs of the integrated energy system, are among the main objectives of the optimization problem. The proposed structure is developed in the form of tractable mixed-integer nonlinear programming (MINLP) problem to handle the day-ahead security-constrained unit commitment (SCUC). The information-gap decision theory (IGDT)-based robust model is used for accurate modeling of wind power uncertainty. The characteristics of the proposed IGDT-based robust SCUC model and its benefits are investigated through several technical case studies conducted on the modified 6-bus and 24-bus test systems. The simulation results validate the effectiveness and feasibility of the proposed structure. According to the obtained results for the 6-bus test system, networked energy hubs can help the system operator to reduce the total operating cost, wind power curtailment cost, and CO2 emission cost by 16.62%, 100%, and 30.44%, respectively, through utilizing up-to-date energy conversion facilities and energy storage systems as well as managing energy demands. It can be seen that the proposed strategy is a very effective step towards achieving a 100% renewable energy system.

A novel robust security constrained unit commitment model considering HVDC regulation

To further improve the robustness and economic efficiency of the conventional security-constrained unit commitment (SCUC) problem when confronting system uncertainties, we propose a novel robust SCUC model considering the prompt and flexible controllability of high voltage direct current (HVDC) in asynchronous interconnected systems. Unlike the conventional method, the proposed model minimizes the total cost of pre-day commitment for the base case and real-time dispatch under extreme uncertainties when guaranteeing the generation units and HVDC can be adjusted adaptively and securely. The robustness and economic efficiency of the model are enhanced by optimizing their trade-off when integrating HVDC regulation. As the method involves unit commitment, short-term dispatch, and power rescheduling, an improved tri-level Benders decomposition algorithm is developed according to the structure of the model to solve this mixed-integer nonlinear programming problem. In computational experiments, the effectiveness of the proposed model was validated in a two-area HVDC interconnected asynchronous system under various uncertainties. Additionally, the superior cooperation between a day-ahead commitment plan and real-time dispatch in the novel SCUC model with HVDC regulation was verified.

A Mixed-Integer Convex Programming Algorithm for Security-Constrained Unit Commitment of Power System with 110-kV Network and Pumped-Storage Hydro Units

The secure operation of 110-kV networks should be considered in the optimal generation dispatch of regional power grids in large central cities. However, since 110-kV lines do not satisfy the premise of R << X in the direct current power flow (DCPF) model, the DCPF, which is mostly applied in the security-constrained unit commitment (SCUC) problem of high-voltage power grids, is no longer suitable for describing the active power flow of regional power grids in large central cities. Hence, the quadratic active power flow (QAPF) model considering the resistance of lines is proposed to describe the network security constraints, and an SCUC model for power system with 110-kV network and pumped-storage hydro (PSH) units is established. The analytical expressions of the spinning reserve (SR) capacity of PSH units are given considering different operational modes, and the SR capacity of PSH units is included in the constraint of the SR capacity requirement of the system. The QAPF is a set of quadratic equality constraints, making the SCUC model a mixed-integer nonlinear non-convex programming (MINNP) model. To reduce the computational complexity of solving the model when applied in actual large-scale regional networks, the QAPF model is relaxed by its convex hull, and the SCUC model is transformed into a mixed-integer convex programming (MICP) model, which can be solved to obtain the global optimal solution efficiently and reliably by the mature commercial solver GUROBI (24.3.3, GAMS Development Corporation, Guangzhou, China). Test results on the IEEE-9 bus system, the PEGASE 89 bus system and the Shenzhen city power grid including the 110-kV network demonstrate that the relaxed QAPF model has good calculation accuracy and efficiency, and it is suitable for solving the SCUC problem in large-scale regional networks.