Unit Commitment Research Articles

Construct watchlist security constraints for monthly unit commitment considering wind power uncertainty

AbstractMonthly unit commitment (UC) is a decision‐making process that determines the optimal generation scheduling of mid and long‐term power system, often operating as a subproblem within monthly electricity market operations and long‐term maintenance scheduling optimization in China. Dealing with transmission constraints in monthly security‐constrained unit commitment (SCUC) is considered to be challenging, but its simplification can be achieved through eliminating inactive security constraints. Additionally, the uncertainty associated with wind power needs to be given due consideration in monthly scheduling. Here, a three‐step watchlist construction strategy is proposed to facilitate the rapid screening of overloaded power flow constraints in monthly unit commitment, including three lists (i.e. list of risk, list of concern, and list of interest) under different conservatism. Importantly, the shift factor method is utilized to screen the potential overloaded constraints arising from the redispatch process for hedging the effects of large wind uncertainties. Results obtained from the testing of IEEE‐118 system indicates that more than 99% of active power flow constraints could be successfully screened out when wind power fluctuation is at 20%. As such, the proposed scheme provides an effective method to consider uncertainty associated with large‐scale wind power generation integrated into the power grid.

Interval reservoir computing: theory and case studies

The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.

Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method

The unit commitment (UC) problem aims to reduce the power generation costs of power generation units in the traditional power system structure. However, under the current arrangement, the problem of cutting the cost of producing electricity has turned into an opportunity to boost power generation units’ profits. Emission concerns are now given considerable weight when talking about the performance planning of power generation units, in addition to economic objectives. Because emissions are viewed as a limitation rather than an objective function in the majority of recent research that has been published in the literature, this paper solves the multi-objective profit-based unit commitment (PBUC) problem while taking into account energy storage systems (ESSs) and renewable energy systems (RESs) in the presence of uncertainty sources, such as demand and energy prices, in order to minimize generated emissions and maximize profits by power generation units in the fiercely competitive energy market. Owing to the intricacy of the optimization problem, a novel mutation-based modified version of the shuffled frog leaping algorithm (SFLA) is suggested as a way to get around the PBUC problem’s difficulty. A 10-unit test system is used for the simulation, which is run for a whole day to demonstrate the effectiveness of the suggested approach. The proposed algorithm’s output is compared with the best-known approaches from various references. The simulated results generated by the suggested algorithms and the previously reported algorithms to solve the PBUC problem show that the proposed method is better than other evolutionary methods utilized in this study and prior investigations. For example, the overall profit from the suggested MSFLA is around 4% and 5.5% higher than that from other algorithms like the ICA and Muller methods in the presence and absence of reserve allocation, respectively. Furthermore, the MSFLA emissions value is approximately 2% and 8% lower than the optimum emissions values obtained using the PSO and ICA approaches, respectively.

A more accurate piecewise linear approximation method for quadratic cost curves of thermal generators and its application in unit commitment

A more accurate piecewise linear approximation method for quadratic cost curves of thermal generators and its application in unit commitment

Transmission expansion planning in a wind-dominated power system: A closed-loop approach

The current paradigm for the transmission expansion planning (TEP) problem follows an open-loop (OL) approach. This approach first executes an accuracy-oriented forecasting method to predict the available wind power, demand, and spinning and non-spinning reserve (SR and NSR) requirements. Afterward, the TEP problem is solved based on these predictions. However, this OL model does not necessarily obtain a better expansion plan in terms of the real system cost (RSC) against the actual realization of wind and demand. This article contributes to the existing literature by developing a closed-loop (CL) strategy for the TEP problem using a cost-oriented prediction method. In this novel cost-oriented methodology, the RSC assesses the forecasting data quality rather than the statistical indices (accuracy-oriented). In this regard, a bilevel programming model is established to train the predicted data to minimize the system RSC. The upper level trains the predictors for wind power and SR/NSR requirements, while the two lower-level problems formulate the unit commitment (UC)-based TEP and the economic redispatch (ERD) problem. The bilevel programming that contains integer variables at both levels is solved by the reformulation and decomposition (R&D) technique. The numerical results on the IEEE 118-bus grid illustrate the cost-effective advantages of the CL-TEP method.

A hybrid evolutionary approach for multi-objective unit commitment problem in power systems

The operating strategy of the power system is changing from the minimum cost to the minimum cost with a minimum emission approach as a result of growing worries about global warming and environmental change brought on by the combustion of fossil fuels. The generating scheduling problem becomes a multi-objective one as a result of the economic and emission objectives' inherent conflict. In this study, a multi-objective whale-differential evolution-genetic algorithm (WODEGA) is employed to produce an optimal solution that illustrates a potential trade-off between the unit commitment (UC) problem's emission and cost objectives. The environmental and economic dispatch problem with system and unit constraints is solved using the multi-objective WODEGA method. Main goal of research is to explore the problem of unit commitment and sub-problem of unit commitment i.e. economic dispatch.

Fast Coordinated Predictive Control for Renewable Energy Integrated Cascade Hydropower System Based on Quantum Neural Network

The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control.

A tri-level Typhoon-DAD robust optimization framework to enhance distribution network resilience

Extreme natural disasters such as typhoon often cause failures in distribution networks within a short time, and even lead to large area blackouts. We propose a tri-level robust optimization framework that combines pre-disaster, in-disaster and post-disaster strategy comprehensively to enhance distribution network resilience. The typhoons are regarded as attackers, and a tri-level Defender-Attacker-Defender model for typhoon disaster is used to integrate multiple resilience resources. In the first level, line hardening and distributed generation unit commitment are used to improve resilience in pre-disaster. The second level contains attack budget, attack time, hardening budget, repair time and load loss. It couples with the third level iteratively to generate the worst failure scenario under typhoon. In the third level, the Nested Column-and-Constraint Generation algorithm is used to solve distribution network reconfiguration and intentional islanding. Simulation time is spanned to the entire 24-hour disaster day. In the end, the proposed framework is tested through a case study using real data from super typhoon “Mangkhut” (2018) in Yangjiang, China. The result shows that it can effectively reduce load loss under the worst typhoon scenario and enhance distribution network resilience with limited resources.

Renewable energy sources integration via machine learning modelling: A systematic literature review

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided.

Unit Commitment Considering Electric Vehicles and Renewable Energy Integration—A CMAES Approach

Global fossil fuel consumption and associated emissions are continuing to increase amid the 2022 energy crisis and environmental pollution and climate change issues are becoming even severer. Aiming at energy saving and emission reduction, in this paper, a new unit commitment model considering electric vehicles and renewable energy integration is established, taking into account the prediction errors of emissions from thermal units and renewable power generations. Furthermore, a new binary CMAES, dubbed BCMAES, which uses a signal function to map sampled individuals is proposed and compared with eight other mapping functions. The proposed model and the BCMAES algorithm are then applied in simulation studies on IEEE 10- and IEEE 118-bus systems, and compared with other popular algorithms such as BPSO, NSGAII, and HS. The results confirm that the proposed BCMAES algorithm outperforms other algorithms for large-scale mixed integer optimization problems with over 1000 dimensions, achieving a more than 1% cost reduction. It is further shown that the use of V2G energy transfer and the integration of renewable energy can significantly reduce both the operation costs and emissions by 5.57% and 13.71%, respectively.

Fuel consumption optimization of a multimachines microgrid by secant method combined with IPPD table

The objective consists in optimizing the fuel consumption of a power station of small cogeneration multi machines while minimizing generated pollution. At first, a study carried out on the strategies of Economic Dispatch , was used to us to solve the unit commitment problem, whose main aim is to determine the optimal schedule of online generating units so as to meet the power demand at minimum operating cost under various system and operating constraints. This work was carried out by Secant method combined with Improved Pre-prepared Power Demand (IPPD) table which obtains the unit status information and then the optimal solution is achieved by Secant method at each power demand for 24 hours. The comparison of results of the proposed method with the results of classical method, by Matlab/Simulink, shows that the proposed approach provides qualitative solution.

A binary bat algorithm with improved crossover operators and Cauchy mutation for unit commitment problem

A binary bat algorithm with improved crossover operators and Cauchy mutation for unit commitment problem

Assessing the Flexibility Potential of Industrial Heat–Electricity Sector Coupling through High-Temperature Heat Pumps: The Case Study of Belgium

Thermal processes represent a significant fraction of industrial energy consumptions, and they rely mainly on fossil fuels. Thanks to technological innovation, highly efficient devices such as high-temperature heat pumps are becoming a promising solution for the electrification of industrial heat. These technologies allow for recovering waste heat sources and upgrading them at temperatures up to 200 °C. Moreover, the coupling of these devices with thermal storage units can unlock the flexibility potential deriving from the industrial sector electrification by means of Demand-Side Management strategies. The aim of this paper is to quantify the impact on the energy system due to the integration of industrial high-temperature heat pumps and thermal storage units by means of a detailed demand–supply model. To do that, the industrial heat demand is investigated through a set of thermal process archetypes. High-temperature heat pumps and thermal storage units for industrial use are included in the open-source unit commitment and optimal dispatch model Dispa-SET used for the representation of the energy system. The case study analyzed is Belgium, and the analysis is performed for different renewable penetration scenarios in 2040 and 2050. The results demonstrate the importance of a proper sizing of the heat pump and thermal storage capacity. Furthermore, it is obtained that the electrification of the thermal demand of industrial processes improves the environmental impact (84% reduction in CO2 emissions), but the positive effect of the energy flexibility provided by the heat pumps is appreciated only in the presence of a very high penetration of renewable energy sources.

Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles: A level-based coupled optimization method

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

Piecewise linear approximation for MILP leveraging piecewise convexity to improve performance

To realize adaptive operation planning with MILP unit commitment, piecewise-linear approximations of the functions that describe the operating behavior of devices in the energy system have to be computed. We present an algorithm to compute a piecewise-linear approximation of a multi-variate non-linear function. The algorithm splits the domain into two regions and approximates each region with a set of hyperplanes that can be translated to a convex set of constraints in MILP. The main advantage of this “piecewise-convex approximation” (PwCA) compared to more general piecewise-linear approximation with simplices is that the MILP representation of PwCA requires only one auxiliary binary variable. For this reason, PwCA yields significantly faster solving times in large MILP problems where the MILP representation of certain functions has to be replicated many times, such as in unit commitment. To quantify the impact on solving time, we compare the performance using PwCA with the performance of simplex approximation with logarithmic formulation and show that PwCA outperforms the latter by a big margin. For this reason, we conclude that PwCA will be a useful tool to set up and solve large MILP problems such as arise in unit commitment and similar engineering optimization problems.

Exact mixed-integer quadratic formulation and solution for large-scale thermal unit commitment

Abstract Thermal unit commitment (UC) is a nonlinear combinatorial optimization problem that minimizes total operating costs while considering system load balance, on/off restrictions and other constraints. Successfully solving the thermal UC problem contributes to a more reliable power system and reduces thermal costs. This paper presents an exact mixed-integer quadratic programming (EMIQP) method for large-scale thermal UC problems. EMIQP revolutionizes the landscape by seamlessly translating the intricate nonlinear combinatorial optimization problem of UC into an exact mixed-integer quadratic formulation. This approach also elegantly reimagines on/off constraints as mixed-integer linear equations, employing both the sum and respective approaches. Our case studies unequivocally demonstrate the exceptional prowess of the EMIQP method, consistently securing the global optimum. Moreover, the mathematical-based EMIQP method produces identical results at each run, which is extremely important for UC in the real world.

Unit commitment and generation dispatch of a hydropower plant in a competitive electricity market

This paper presents a model to solve the short-term scheduling problem of a hydropower plant in a deregulated system, based on dynamic programming techniques. The objective of this model is to maximize the revenue obtained by selling energy in a competitive electricity market. The time horizon of the model is divided into hourly periods and ranges from one day to one week. The proposed model determines both the unit commitment and the power to be generated in each hour of the time horizon. The power is considered as a nonlinear function of the water discharge and the reservoir volume; the dependence of the units and plant operating limits on the available gross head has been taken into account; and the water discharged through the spillway has been also allowed for in the model. This approach has been applied to a practical case study, the results of which are analyzed in detail.

Stochastic unit commitment based on energy‐intensive loads participating in wind and solar power consumption

AbstractThe fluctuation and intermittency of wind and solar power outputs result in increased regulation pressure on thermal units in power systems. Adjustable energy‐intensive loads (such as electrolytic aluminium and steel plants) have great potential for participating in demand response (DR) programs with the goal of reducing thermal unit regulation pressure. This paper proposes an optimal scheduling method of unit commitment (UC) which gives consideration to energy‐intensive loads participating in wind and solar power consumption. The UC method adopts the nonparametric kernel density estimation method to model wind and solar power outputs and then uses the Frank‐Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces a chance‐constrained theory of a reserve coefficient to describe time‐variant scenarios on the basis of the deviation between the typical and simulative scenarios. The simulation results based on the IEEE 118‐bus system show that the energy‐intensive load in the SUC model can flexibly adjust and respond to changes in wind and solar power output, reduce the impact of the uncertainties of wind and solar power output, and promote the consumption of wind and solar power.

Reliability based computational model for stochastic unit commitment of a bulk power system integrated with volatile wind power

Reliable and economic operation of transmission systems is one of the most onerous problems faced by the grid operators as a penetration rate of wind energy is on rise. It is a crucial carbon-free source and alleviates dependency upon the conventional resources, though its stochastic and unpredictable nature can cause the risk of random component failures. Therefore, reliability evaluation of the system under wind power is of paramount importance and is suggested as the proposed study. A Reliability based Stochastic Unit Commitment (RSUC) model is proposed for a bulk power system integrated with volatile wind power. It is formulated using Mixed Integer Linear problem considering networks and spinning reserve constraints. The volatility of wind speed is modeled by generating different scenarios using K-means clustering approach with computation of correlation among them using autocorrelation factor and their transition probabilities is proposed using Markov Chain (MC). Further, the system's reliability is evaluated using Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). The proposed approach is investigated on the IEEE 24 Reliability Test System (RTS) under the various overloading and random component outage conditions. Further, its results effectiveness is compared and validated with the other existing approaches.

An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties.

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considerssix various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effectivethan itspeers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.