Stochastic Security Constrained Unit Commitment Research Articles

Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations

Day-ahead operation involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, South-Carolina (SC) synthetic grid 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation. Simulation results demonstrate a high training accuracy to identify commitment schedule while FL and post-process ensure ML predictions do not lead to infeasible solutions with minimal loss in solution quality.

Risk-constrained probabilistic coordination in coupled transmission and distribution system

High penetration of renewable energy has instigated stochastic power injection at interconnection between transmission system (TS) and distribution system (DS). This paper delves into the intricate collaborative risk based stochastic dispatching challenges due to contingencies and the integration of renewable sources in the TS and DS. A framework of risk constrained probabilistic problem (RCPC) is presented, which is divided into Probabilistic coordination (PC) and risk-based stochastic security-constrained unit commitment (RSSCUC). The PC is a stochastic linear problem and RSSCUC is a stochastic mixed integer non-linear problem. Therefore, the probabilistic analytical target cascading along with polynomial chaos expansion (PCE) has been utilized for solving the PC problem for coupled TS and DS. Here, PCE has provided the proposed algorithm with the needed capability to present stochastic shared variables as coefficients for PC. Further, the benders decomposition algorithm has been utilized for solving RSSCUC, by dividing it into master problem and sub problem to generate feasibility cuts. The proposed technique reduces the computational burden on the system, as it requires single stochastic coordination problem for all scenarios as an alternative of each coordination problem for each scenario and polynomial coefficients instead of multiple scenarios for each shared variable between TS and DS. Different case studies have been performed utilizing the 6-bus system and IEEE 118-bus system as TSs, 7-bus, 9-bus, 85-bus and 69-bus systems as DSs. Results depict the efficacy of the proposed method.

A novel framework to assess synthetic inertia & primary frequency response support from energy storage systems

Grid integration of large scale uncertain Renewable Energy (RE) generation reduces the share of conventional generation, and thereby reduces the overall system inertia (SI) and Primary Frequency Response (PFR). This necessitates integration of additional sources of inertia and PFR for rapid generation-demand balancing during inevitable contingencies. Due to high-power density and fast response, Energy storage systems (ESS) are evolving as a potential solution for quick grid balancing. Technical viability of these sources to facilitate synthetic inertia and PFR is well established. However, to ensure inertial and PFR adequacy in operational time frame, the Frequency Response (FR) support from these sources must be integrated in Security Constrained Unit Commitment (SCUC) and dispatch decisions. In this context, the present work proposes a Stochastic SCUC framework to model synthetic inertia and PFR support from ESS. Synthetic inertia and PFR support from fast acting ESS are mapped with post-fault frequency dynamics to handle system frequency stability. Further, a machine learning-based Bayesian inference statistic using Markov Chain Monte Carlo algorithm is used to characterize and represent RE uncertainty in scheduling framework. Case studies are performed on IEEE 6 bus and New England 39 bus test systems under various RE integration scenarios to demonstrate the effectiveness of proposed framework. Simulation results highlight ESS potential to significantly enhance post-fault frequency stability in terms of rate of change of frequency, frequency nadir, nadir time, and steady-state frequency.

Stochastic Security-constrained Unit Commitment Considering Electric Vehicles, Energy Storage Systems, and Flexible Loads with Renewable Energy Resources

Stochastic Security-constrained Unit Commitment Considering Electric Vehicles, Energy Storage Systems, and Flexible Loads with Renewable Energy Resources

Battery energy storage train routing and security constrained unit commitment under solar uncertainty

The location of solar parks far from load areas may lead to transmission congestion and thus solar curtailment for secure system operation. Battery energy storage (BES) Train as mobile storage can transmit solar energy from site to load centers using a transport network while relieving lines from congestion. Therefore, stochastic security-constrained unit commitment (SCUC) with BES Train is modeled in this paper under solar uncertainty and N-1 critical line contingency. Time-space network models transportation constraints of BES Train routing problem. To reduce the computational burden, this paper proposes modeling of line outage distribution factor (LODF) for DC power flow that reduces the nonzero coefficients of post-contingency network constraints. The benders decomposition technique simplifies stochastic optimization for the mixed-integer linear programming (MILP) problem. BES Train integrated modified IEEE reliability test and 118-bus system is investigated as a case study. Simulations evaluate the effect of BES Train, solar uncertainty and critical line contingency state with LODF on operation costs, BES Train scheduling, solar curtailment, congestion and computational time. The proposed model shows that BES Train is an economical option for large solar integration to reduce transmission congestion and curtail solar power effectively.

Synthetic inertia and frequency support assessment from renewable plants in low carbon grids

Increasing grid integration of uncertain Renewable Energy (RE) sources reduces system’s inherent Frequency Response (FR) and restricts System Operators’(SOs) ability to maintain frequency stability. This condition further aggravates as existing operational practices do not consider FR from RE based generation. Technical feasibility of RE generation to facilitate synthetic inertia and Primary Frequency Response (PFR) is well established. However, FR support from RE plants must be integrated in scheduling decisions to ensure inertial and PFR adequacy in operational time frame. Further, unlike conventional units, RE plants’ output is volatile due to inherent variability and uncertainty. Hence, the uncertainty of RE generation needs to be considered in scheduling horizon to accurately assess system-wide FR requirement and RE Plants’ potential in its’ contribution. In this context, the paper proposes a novel day-ahead stochastic security-constrained unit commitment framework to incorporate synthetic inertia and PFR support from wind and PV plants, and ensure frequency response adequacy. RE generation is forecasted using long-short-term memory, a deep learning approach. A machine learning-based Bayesian inference statistics is used to characterize RE power uncertainty. Simulations are carried out on New England test system for various RE integration levels. Numerical results demonstrate the effectiveness of frequency support from RE plants in terms of improvement in frequency security parameters. Operating cost and RE curtailment are reduced 11% and 68%, respectively with PFR support from RE plants.

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

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.

Stochastic Procurement of Fast Reserve Services in Renewable Integrated Power Systems

Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time.

A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N − 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied.

An Enhanced Contingency-Based Model for Joint Energy and Reserve Markets Operation by Considering Wind and Energy Storage Systems

This article presents a contingency-based stochastic security-constrained unit commitment to address the integration of wind power producers to the joint energy and reserve markets. The model considers ancillary services as a solution to cope with the uncertainties of the problem. In this regard, a comprehensive model is considered that maintains the profit of supplementary services. The contingency ranking is a popular method for reducing the computation burden of the unit commitment problem, but performing the contingency analysis changes the high-impact events in previous ranking methods. This article employs an intelligent contingency ranking technique to address the above issue and to find the actual top-ranked outages based on the final solution. The proposed algorithm simultaneously clears the energy and reserve based on the mechanism of the day-ahead market. The main idea of this article is to develop a framework for considering the most effective outages in the presence of the uncertainty of wind power without a heavy computation burden. Also, energy storage systems are considered to evaluate the impact of the scheduling of storage under uncertainties. Also, an accelerated Benders decomposition technique is applied to solve the problem. Numerical results on a six-bus and the IEEE 118-bus test systems show the effectiveness of the proposed approach. Furthermore, it shows that utilizing both wind farms and storage devices will reduce the total operational cost of the system, while the intelligent contingency ranking analysis and enough reserves ensure the security of power supply.

PFR constrained energy storage and interruptible load scheduling under high RE penetration

Large-scale renewable energy (RE) integration in the power systems is displacing conventional sources of generation, thus reducing the system's inherent inertia and primary frequency response (PFR) capabilities. This reduced response may decline the system's ability to stabilise frequency after inevitable events such as loss of generation or load and thus requires additional PFR. To meet the PFR requirements, fast-acting resources like energy storage systems (ESS) and demand response (DR) (transferrable and interruptible loads) need to be integrated within the system. In this regard, a novel stochastic security-constrained unit commitment framework is proposed to assess the potential of ESS and DR to ensure additional PFR requirements and handle post contingency frequency dynamics. Moreover, the proposed methodology analyses the impact of uncertain RE integration on system frequency dynamics using Bayesian inference statistics. Case studies are carried out on 39 bus New England and 118 bus IEEE test systems, incorporating post fault frequency security parameters. Simulation results analyse the PFR contribution of ESS and DR and indicate a substantial reduction in system operating cost and RE curtailment with their integration. The proposed analysis would help system operators to maintain required PFR under high RE penetration, and handle unavoidable contingencies.

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.

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.

Hybrid Search Optimization Algorithms for the Security Constrained Unit Commitment Solution

The effect of a lot of wind has convoluted ramifications to Unit Commitment (UC) and Economic Dispatch (ED). The Security Constrained Unit Commitment (SCUC) is utilized to acquire a UC with the base expense and framework's security. Ordinarily, the SCUC's limitations incorporate the heap balance, the hold prerequisite, slope rate limits, minimum up and down time limits and network constraints. In this paper, Memetic Algorithm (MM) algorithm based Prediction Intervals (PIs) are executed utilizing hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) based Lower–Upper Bound Estimation (LUBE) technique for wind power conjecture vulnerability evaluation. At that point the wind control situations are fused into a SCUC model. Hybrid MM-Bee Colony algorithm is utilized to take care of the stochastic SCUC issue.

Hybrid Search Optimization Algorithms for the Security Constrained Unit Commitment Solution

The effect of a lot of wind has convoluted ramifications to Unit Commitment (UC) and Economic Dispatch (ED). The Security Constrained Unit Commitment (SCUC) is utilized to acquire a UC with the base expense and framework's security. Ordinarily, the SCUC's limitations incorporate the heap balance, the hold prerequisite, slope rate limits, minimum up and down time limits and network constraints. In this paper, Memetic Algorithm (MM) algorithm based Prediction Intervals (PIs) are executed utilizing hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) based Lower–Upper Bound Estimation (LUBE) technique for wind power conjecture vulnerability evaluation. At that point the wind control situations are fused into a SCUC model. Hybrid MM-Bee Colony algorithm is utilized to take care of the stochastic SCUC issue.

Chance-constrained CAES and DRP scheduling to maximize wind power harvesting in congested transmission systems considering operational flexibility

As enlarging share of renewables brings up a promising future for clean power generation, nonetheless, it imposes new challenges into the secure operation of power systems such occurrence of distasteful congestion, discriminatory locational marginal pricing (LMP) and also increasing uncertainty and inflexibility. To address these issues, a novel chance constrained two-stage programming is developed, where in the first stage social welfare of system is maximised while in the second stage a stochastic security constrained unit commitment problem is executed along with compressed air energy storage (CAES) and demand response program (DRP) to minimize both operation costs and wind curtailment. Both DRP and CAES are cooperatively applied to maximize wind proliferation and social welfare, alleviate the congestion of network, smooth LMP at different nodes, and improve technical characterizations of system. The problem is formulated as an exact mixed integer non-linear programming (MINLP) considering operational flexibility by means of power capacity for up/down power regulation and then is solved using primal-dual interior point solver. Finally, a case study based on modified IEEE 30-bus transmission system with three zones is performed and the results are duly expressed and analysed to corroborate the pertinence of the proposed model.

Stochastic scheduling of battery energy storage system for large‐scale wind power penetration

High penetrations of wind generation have intermittency and volatility issues in deregulated power system. With increasing penetration of wind power, independent system operator (ISO) faces an enormous challenge in secure operation with intermittency and uncertainty of renewable energy resources. To handle these issues, battery energy storage system (BESS) is a probable solution for enhancing the wind penetration in the electric grid. In this regard, this article proposes a stochastic security constrained unit commitment (SCUC) with BESS considering wind uncertainty. The proposed approach is formulated and solved by Benders decomposition (BD) method which reduces computational time. The goal is to detect the optimal amount of SCUC, locational marginal price (LMP), wind curtailment, and line contingency by performing DC optimal power flow (DCOPF). Numerical case studies are investigated in detail using IEEE Reliability test system (RTS) 24-bus network and simulation results are found to be inspiring.

Stochastic scheduling of compressed air energy storage in DC SCUC framework for high wind penetration

High intermittent wind generation necessitates integration of bulk energy storage systems (ESSs) for maintaining security and reliability in power system operation. Considering this, stochastic security constrained unit commitment (SCUC) including compressed air energy storage (CAES) as bulk ESS for high wind penetration and with wind uncertainty modelling is addressed. Network constraints for pre- and post-line contingency are modelled using DC power flow. Injection sensitivity factors (ISFs) are conventionally used in power flow equations which, however, make N -1 network security constrained formulation huge and computationally demanding for the proposed stochastic model. Therefore, this study proposes a line outage distribution factor (LODF) to reduce the number of coefficients of post contingency DC power flow equations. This is a compact formulation with the lower computational requirement. Wind uncertainty is modelled as probable scenarios. The proposed SCUC is a complex mixed integer linear programming problem and solved using Benders decomposition technique for IEEE 30-bus and 118-bus system. Simulation results to analyse the impact of CAES, wind uncertainty and line contingency with ISF and LODF on overall operation costs, CAES scheduling, wind curtailment, locational marginal price and computational time. Results show that the proposed model is computationally efficient for system operation under high wind penetration.

A Frank–Wolfe Progressive Hedging Algorithm for Improved Lower Bounds in Stochastic SCUC

The instantaneous penetration of renewable generation, such as wind and solar generation, reaches over 50% in certain balancing areas in the United States. These generation resources are inherently characterized by uncertainties and variabilities in their output. Stochastic security-constrained unit commitment (S-SCUC) using a progressive hedging algorithm (PHA) has been utilized to schedule the generation resources under uncertainties. However, dual bounds obtained in the PHA are sensitive to the penalty factor chosen, and the convergence of the PHA is problematic due to the existence of integer decisions. In this paper, we apply a novel Frank–Wolfe-based simplicial decomposition method in conjunction with the PHA (FW-PHA) to improve the quality of dual bounds and the convergence characteristics in solving the S-SCUC. The numerical tests are carried out on the IEEE RTS-96 and IEEE 118-bus systems. The numerical results show the effectiveness of the proposed FW-PHA-based S-SCUC. In comparison with the traditional PHA, the proposed algorithm converges to a tighter dual bound and is robust to any penalty factor selected.