Security-constrained Unit Commitment Research Articles

Security Constrained Unit Commitment Based on Modified Line Outage Distribution Factors

The N-1 criterion is universally taken as the security constrains in the security-constrained unit commitment (SCUC) problem. The line outage distribution factors (LODF) is widely used in the N-1 continency for its state independence and computational efficiency. However, the LODF based model fails to consider reactive power and voltage magnitude. This paper closes this gap by deriving modified line outage distribution factors (MLODF) based on a linear power flow model. The proposed model, though state-independent, provides high-quality approximation of voltage magnitudes. Then, we apply this model to formulate the SCUC problem and propose an iterative solution process based on MLODF post-contingency filter to set up the contingency constraints. The simulation results of the benchmark test systems verify the accuracy of proposed MLODF. The six-bus and IEEE 118-bus systems show that the MLODF based model can provide a more accurate and secure generation schedule comparing with the traditional LODF based model while ensuring the AC feasibility for most N-1 continency.

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.

A Security-Constrained Hydrothermal Unit Commitment Model in the Day-Ahead Electricity Market

A Security-Constrained Hydrothermal Unit Commitment Model in the Day-Ahead Electricity Market

A Multi-Cut L-Shaped Decomposition Algorithm for Stochastic Security Constrained Unit Commitment with Renewable Generation

A Multi-Cut L-Shaped Decomposition Algorithm for Stochastic Security Constrained Unit Commitment with Renewable Generation

Sizing and Siting of Battery Energy Storage Systems: A Colombian Case

This paper presents a mixed-integer linear programming (MILP) formulation for sizing and siting of battery energy storage systems (BESSs). The problem formulation seeks to minimize both operation costs and BESS investment. The proposed model includes restrictions of the conventional security-constrained unit commitment problem, a piece-wise linear approximation to model power losses, and a linear model of hydro generation units. The proposed model is tested in a 6-bus test system and a 15-bus system representing the Colombian power system. For the two studied systems, simulation results show that the reduction of operation costs due to the installation of BESSs compensates the investments, under some of the considered technical cost cases. Additionally, results show that adequate sizing and siting of BESSs reduce renewable energy curtailment in the Colombian power system with high penetration of fluctuating renewable generation.

Robust flexibility driven security constrained unit commitment under wind uncertainty considering demand response and combined-cycle units

Operational flexibility aiming to mitigate variability impacts of Renewable Energy Resources (RES) inherently depends on inter-temporal operational status. Accordingly, in the current paper, a novel operational flexibility maximization procedure is developed within a robust Security Constrained Unit Commitment (SCUC) problem to find the most tolerable flexibility band in response to RES variability impacts. In addition, combined-cycle units (CCU) and incentive based demand response, from both generation and demand sides, are considered as flexibility providers because of their specific characteristics such as high ramping capacities and fast start-up/shut-down rates. However, unlike conventional units with single mode operation, a CCU unit is composedof multiple combustion turbines (CTs) and steamturbines (STs) that can operate in different modes. Since it is needed to decide the on/off statusand power output of each CT and ST unit at every timeinterval, the recourse decision layer will be non-convex. The challenging non-convex recourse decision level is cast as a mixed-integer program that is solved by an exact nested column-and-constraint generation algorithm. The capability of the proposed model is then investigated in standard IEEE 118-bus test system.

Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency

Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security-constrained unit commitment (SCUC) model to clear day-ahead energy and reserve markets considering transportation–electricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre-contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high-impact outages. To address this issue, generator outages ranked first are identified using the post-contingency generator response ranking approach. The main contribution of this paper is the development of a structure with the most effective outages in the presence of uncertain wind power and transportation networks. The stochastic optimization approach is modeled through scenarios with corresponding probabilities to manage the wind uncertainty. The computationally complex proposed model is solved by a prominent bender decomposition approach. Case studies are shown on a modified IEEE 118-bus system with 26 nodes and 74 connected links of the electricity transportation network. The results show that optimal EV travel scheduling can optimize the transportation network and a sufficient energy reserve, confirming the security of the power system with minimum operating cost.

Deep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities

This paper proposes an intelligent Deep Learning (DL) based approach for Data-Driven Security-Constrained Unit Commitment (DD-SCUC) decision-making. The proposed approach includes data pre-processing and a two-stage decision-making process. Firstly, historical data is accumulated and pre-processed. Then, the DD-SCUC model is created based on the Gated Recurrent Unit-Neural Network (GRU-NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two-stage decision-making process outputs the decision results based on various applications and scenarios. This approach has self-learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118-bus test system and a real power system from China showed that compared with deterministic Physical-Model-Driven (PMD)-SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.

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.

Security constrained unit commitment with continuous time-varying reserves

This paper presents a security constrained unit commitment (SCUC) with continuous intra-hour time-varying reserves. The hourly formulation extends the power-based UC formulation and has reserves which vary continuously within the hour, as opposed to the traditional hourly energy-based SCUC that uses constant reserves within the hour. We show that the traditional hourly energy-based formulation cannot ensure N-1 security at all times, since this formulation is not able to take the power trajectories of units within the hour into account. This is remedied by an hourly power-based version which allows the formulation of contingency constraints to guarantee N-1 security at all times within the hour. The proposed formulation uses continuous time-varying reserves which lowers the cost for providing reserves and makes better use of units’ flexibility while still ensuring N-1 security. The energy-based and power-based formulations are evaluated using different versions of a 5-min security-constrained economic dispatch (SCED) based on real load data, thus simulating the real time operation of the system under different assumptions for reserve procurement. The results show that the power-based formulation increases security compared to the energy-based formulation, both if reserves are fixed to the values from the SCUC or co-optimized in real time by the SCED.

Multi-objective Mathematical Programming to Simultaneously Control SCUC and OUPFC to Improve Power System Controllability

ABSTRACT Security-Constrained Unit Commitment (SCUC) problem and subsequently employing Flexible AC Transmission Systems (FACTS) controllers including Optimal Unified Power Flow Controller (OUPFC) play a crucial role in power systems operation and control in terms of increasing the security of energy transmission networks. The principal objective of the SCUC problem is to acquire the minimum operating cost simultaneously maintaining the security of the system. In this paper, significant attention is paid particularly to simultaneously control SCUC and OUPFC, since energy loss reduction and transmission lines capacity release can be effectively achieved with incorporating SCUC and OUPFC, and subsequently setting the effective variables. In this regard, the SCUC problem with the presence of OUPFC is solved as Nonlinear Programming with Discontinuous Derivatives (DNLP) framework and the proposed algorithm is implemented on two IEEE 14-bus and IEEE 30-bus systems using General Algebraic Modeling System (GAMS) software in order to optimize the total fuel cost, power losses and cost of OUPFC installation as objective functions in the multi-objective framework. The simulation results show the capability of the proposed algorithm to simultaneously determine the optimal location of the OUPFC and its settings in the SCUC problem and to improve the controllability of the power system operation.

A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty

Currently, optimization models for the safe and reliable operation of power systems deal with two major challenges: the first one is the reduction of the computational load when considering N−1 contingencies; the second one is the adequate modeling of the uncertainty of intermittent generation and demand. This paper proposes a new affinely adjustable robust model to solve the security constrained unit commitment problem considering these sources of uncertainty. Linear decision rules, which take into account the forecasts and forecast errors of the different sources of uncertainty, are used for the affine formulation of the dispatch variables, thus allowing the tractability of the model. Another major novelty is that the evaluation of the N−1 security constraints is performed by incorporating a novel method, proposed in the literature, based on the user-cuts concept. This method efficiently and dynamically adds only the binding N−1 security constraints, increasing the computational efficiency of the model when transmission line contingencies are considered. Finally, Monte Carlo simulations on the post-optimization results were run to demonstrate the effectiveness, feasibility and robustness of the solutions provided by the proposed model.

Simultaneous optimal short term SCUC solution and power system redesign with real gap decomposition technique

In this work, firstly, a decomposition technique is proposed and applied to solve the Security Constrained Unit Commitment problem (SCUC). A recently developed model for optimal short term programming of power generation in thermoelectric plants including transmission constraints is adopted. Real gap is computed at the final solution. Secondly, a method to analyze results and redesign the system structure is also proposed. By applying this method, critical lines and generators can be detected, and then, network improvements can be suggested by either eliminating or duplicating lines or generators. With the aim of studying the efficiency of the proposed method, two systems of different sizes are considered. The efficiency of the proposed technique is compared with the direct resolution of the original model and a linearly approximate model. The proposed decomposition technique presents a good performance: optimal solutions are obtained within small real gaps and efficient computing times. In power systems, the SCUC problem is solved every 60 minutes or less; therefore, the resolution time sought must be much less than that. Without applying the decomposition technique, the resolution of the SCUC problem takes several hours to reach an acceptable gap; and instead, when applying the decomposition technique; smaller gaps can be obtained in a few minutes.

Risk-constrained non-probabilistic scheduling of coordinated power-to-gas conversion facility and natural gas storage in power and gas based energy systems

Abstract Increasing environmental concerns related to fossil fuels has led to an increased desire to utilize renewable energy in the power system. Wind power resources as clean energy have an important role in the long-term energy landscape. However, the probabilistic nature of these resources will make several challenges from the integration point of view with the modern power system. Power-to-gas (P2G) technology as an emerging storage system by converting the power generated from the wind power into natural gas (NG) plays a significant role in integrating more and more wind power into the power system. Besides, the NG-fired (NGF) units have received much attention due to faster response, higher efficiency, and far lower pollution emissions. The emergence of P2G and NGF units has led to the interdependency of NG and electrical networks. This interconnection requires the simultaneous optimization and operation of both networks considering all relevant restrictions. Therefore, this paper presents a developed security-constrained unit commitment (SCUC) problem for integrated power and NG networks in the presence of P2G, NG storage, and wind power resources. The main objective of the proposed problem is to minimize the operation cost by considering all the characteristics and interconnections between the power and NG networks. To handle the uncertainty related to wind power, the information gap decision theory (IGDT)-based non-probabilistic robust approach is applied. The proposed model is implemented on the integrated 6-bus electrical and 6-node NG networks and integrated 24-bus electrical and 10-node NG networks for different cases and numerical results reveal the effectiveness of the proposed approach.

Scalable Security-Constrained Unit Commitment Under Uncertainty via Cone Programming Relaxation

This paper is concerned with the problem of Security-Constrained Unit Commitment (SCUC) which is a long-standing challenge in power system engineering faced by system operators and utility companies on a daily basis. We consider a detailed variant of this problem that suffers from complexities posed by the presence of binary variables, the uncertainty of renewable sources and security constraints. A convex relaxation is formulated which is capable of finding feasible solutions within a provable distance from global optimality. We demonstrate the performance of this approach on detailed and challenging instances of SCUC with IEEE and PEGASE benchmark cases from Matpower . The proposed approach is able to handle over 12,000 binary variables and 2 million continuous variables with significant improvement in solution quality over commonly-used off-the-shelf solvers and other methods of convex relaxation.

A clearing mechanism for joint energy and ancillary services in non-convex markets considering high penetration of renewable energy sources

This paper introduces a day-ahead clearing model for the simultaneous energy and ancillary services market in high penetration of renewable energy sources (RESs) considering fast-start generators’ behaviors as non-spinning reserve providers. For managing the uncertainties of RESs, the system operator takes the advantages of the stochastic model of security-constrained unit commitment (SCUC) problem considering plausible scenarios. The non-convexities due to the startup costs, minimum power outputs, and commitment variables make the stochastic SCUC problem and market-clearing non-convex. Meanwhile, with traditional market-clearing methods for energy and ancillary services based on marginal costs or dual variables, the net profit of some generators may become negative. In this situation, more financial losses will be imposed on fast-start generators since they are marginal/intermittent units for managing the uncertainties of RESs. Using a duality gap minimization and profit insurance, this paper presents a pricing model to solve these issues. The proposed market-clearing model leads to uniform prices for ancillary services and nodal prices for energy guaranteeing the non-negative net profit of all generators. The results of the 3-buses system and IEEE 118-buses system are analyzed to illustrate the applications of the proposed pricing approach.

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.

Graph computing based security constrained unit commitment in hydro-thermal power systems incorporating pumped hydro storage

This paper proposes a graph computing based mixed integer programming (MIP) framework for solving the security constrained unit commitment (SCUC) problem in hydro-thermal power systems incorporating pumped hydro storage (PHS). The proposed graph computing-based MIP framework considers the economic operations of thermal units, cascade hydropower stations and PHS stations, as well as their technical impacts towards the network security. First, the hydro-thermal power system data and unit information are stored in a graph structure with nodes and edges, which enables nodal and hierarchical parallel computing for the unit commitment (UC) solution calculation and network security analysis. A MIP model is then formulated to solve the SCUC problem with the mathematical models of thermal units, cascade hydropower stations and PHS stations. In addition, two optimization approaches including convex hull reformulation (CHR) and special ordered set (SOS) methods are introduced for speeding up the MIP calculation procedure. To ensure the system stability under the derived UC solution, a parallelized graph power flow (PGPF) algorithm is proposed for the hydro-thermal power system network security analysis. Finally, case studies of the IEEE 118-bus system and a practical 2749-bus hydro-thermal power system are introduced to demonstrate the feasibility and validity of the proposed graph computing-based MIP framework.

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

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

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

An AC security constrained unit commitment (AC-SCUC) in the presence of the renewable energy sources (RESs) and shunt flexible AC transmission system (FACTS) devices is conventionally modeled as a deterministic optimization problem to minimize the operation cost of conventional generation units (CGUs) subject to AC optimal power flow (AC-OPF) equations, operation constraints of RESs, shunt FACTS devices, and CGUs. To cope with the uncertainties of load and RES generation, robust and stochastic optimization and linearized formulation have been used to achieve a sub-optimal solution. To arrive at a more optimal solution, an evolutionary algorithm-based adaptive robust optimization (EA-ARO) approach to solve the non-linear and non-convex optimization problem was proposed. A hybrid solver of grey wolf optimization (GWO) and teaching learning-based optimization (TLBO) was proposed to solve the AC-SCUC problem in the worst-case scenario to obtain robust and reliable optimal solution. Finally, the proposed method was simulated on standard IEEE test systems to demonstrate its capabilities, and the results showed the proposed hybrid solver obtained robust optimal solutions with reduced computation time and standard deviation. Moreover, the numerical results proved the proposed strategy’s capabilities of improving the economics of generation units, such as lower operational cost, and enhancing the performance of the transmission networks, such as improved voltage profile and reduced energy losses.