Unit Commitment Strategy Research Articles

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittentwindpower. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations.

Economic Operation of Low Voltage Smart Micro-grid with Integration of Renewable Energy

This paper develops a unit commitment multi-period energy management system to minimize a low voltage microgrid's total operation and emission cost. The optimization problem is formulated in the mixed-integer quadratic program. The environment cost and battery degradation cost are taken into consideration in the proposed optimization approach. The unit commitment strategy is employed to minimize the total cost. A set of constraints are considered in the proposed optimization approach. The proposed energy management system is applied to the low voltage distribution grid, including different distributed generators, such as diesel engines, fuel cells, and microturbines. The microgrid also contains storage batteries, renewable energy resources, wind turbines, and photovoltaic panels. The results reveal that the storage battery charging and discharging operations are controlled to reduce the operation and emission cost even considering the battery degradation cost.

Optimal battery cycling strategies in workplaces with electric vehicle chargers, energy storage systems and renewable energy generation

Efficient unit commitment strategy in a modern workplace facilitated with electric vehicle (EV) chargers and energy storage systems requires implementation of optimal battery cycling for both local storage system and electric vehicle batteries. In order to achieve this goal, it is necessary to address the battery health in the energy management strategies of commercial buildings. A fair battery cycling approach that could consider the interests of both parties in a workplace (including the system operator and the EV owners) requires access to detailed information on battery performance and degradation-associated costs. In this study, a detailed investigation is carried out on the optimal battery cycling in a workplace that is facilitated with an EV charging station, energy storage system and renewable energy generation. This is carried out by employment of a tailored unit commitment model that can address the battery health for EVs, individually. This study illustrates how a business owner and the employees that own electric vehicles can benefit from bidirectional battery cycling in an equitable way without compromising their financial interests in the energy market.

A Novel way out to Unit Commitment Problem utilizing Evolutionary Particle Swarm Optimization

This paper differentiates the shows of three Unit Commitment strategies, three of which are the essential arrangement techniques for taking care of the Unit Commitment Problem named Priority List and Dynamic Programming strategies. The third strategy is the Evolutionary Particle Swarm Optimization method which has been applied productively to a plentiful blend of streamlining issues. Various regions in control frameworks require understanding at least one nonlinear streamlining emergencies. In spite of the way that systematic techniques may experience moderate intermingling and the scourge of dimensionality, heuristics-based swarm knowledge can be a capable substitute. Evolutionary Particle Swarm Optimization (EPSO), some portion of the swarm insight family, is known to adequately take care of enormous scale nonlinear improvement issues. This paper introduces the exit plan for Unit Commitment Problem by methods for EPSO system. A calculation was created to achieve an exit plan to the Unit Commitment Problem utilizing EPSO procedure. The adequacy of the calculation is tried on three generating units and the cultivated results utilizing the three techniques are thought about for complete working expense.

Defining Key Parameters of Economic and Environmentally Efficient Residential Microgrid Operation

Abstract Aggregating consumers and distributed generation on the same location with coupled centralized control is the main advantage of a microgrid concept. If these consumers do not have the ability to balance the variability of the renewable energy sources (RES) production the microgrid can be perceived from the distribution system point of view as a potential imbalance source. Evaluating the potential flexibility benefits of different units in the microgrid provides a valuable step towards a successful integration of renewable energy sources. This paper provides insight into different flexibility drivers of microgrid operation simulated in a developed mixed integer linear (MILP) model. The analyses focus on defining the impact of different storage size, control and location as well as different cogeneration unit technologies and efficiencies. These impacts are evaluated through several defined microgrid flexibility indicators, wasted heat and curtailed wind, considering operational techno-economic constraints of different microgrid components (battery storage, heat storage, micro combined heat and power units (μCHP)). Finally the interaction of the microgrid with the distribution system through the point of common coupling (PCC) in an hourly operation controlled by the rolling horizon unit commitment strategy is shortly described.

Multiobjective multiarea unit commitment using hybrid differential evolution algorithm considering import/export and tie-line constraints

Recent power system networks are characterized by large proportions, high interconnections, and high nonlinearities. Challenge of supplying the nation with high-quality, reliable electrical energy at a reasonable cost converted government policy into deregulation and restructuring environment. To achieve significant cost-savings, multiarea unit commitment strategies are employed, whose intention is to establish the optimal commitment stratagem for power generating units situated in numerous areas which are interconnected through tie-lines, and combined operation of generation resources can result in considerable operational cost-savings. Differential evolution is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it gorgeous for optimization. Differential evolution uses somewhat greedy and less stochastic approach for optimization problem solution. Although global exploration ability of differential evolution (DE) algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low, and it suffers from the problem of untimely convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of differential evolution algorithm combined with random search algorithm is presented in the proposed research to solve multiobjective and multiarea unit commitment problem of electric power system. The performance of the proposed hybrid algorithm is tested with benchmark of three-area interconnected system, which consist of IEEE-30 Bus system. Experimental results show that proposed technique has the prospective for the solution of multiobjective and multiarea unit commitment problem and power generation scheduling in deregulated electricity market with import and export constraints.

Risk-Based Admissibility Assessment of Wind Generation Integrated into a Bulk Power System

The increasing integration of large-scale volatile and uncertain wind generation has brought great challenges to power system operations. In this paper, a risk-based admissibility assessment approach is proposed to quantitatively evaluate how much wind generation can be accommodated by the bulk power system under a given unit commitment (UC) strategy. Firstly, the operational risk brought by the variation and uncertainty of wind generation is developed as an admissibility measure of wind generation. Then its linear approximation is derived for practical implementation. Furthermore, a risk-minimization model is established to mathematically characterize the admissible region of wind generation. This model can be solved effectively by a modified column and constraint generation (C&CG) algorithm. Simulations on the IEEE 9-bus system and the real Guangdong power grid demonstrate the effectiveness and efficiency of the proposed methodology.

An economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response

Demand response (DR) and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems. However, the uncertain wind power generation (WG) which has anti-peaking characteristic would be hard to exert its ability in carbon reduction. This paper introduces DR into traditional unit commitment (UC) strategy and proposes a multi-objective day-ahead optimal scheduling model for wind farm integrated power systems, since incentive-based DR can accommodate excess wind power and can be used as a source of system spinning reserve to alleviate generation side reserve pressure during both peak and valley load periods. Firstly, net load curve is obtained by forecasting load and wind power output. Then, considering the behavior of DR, a day-ahead optimal dispatching scheme is proposed with objectives of minimum generating cost and carbon emission. Non-dominated sorting genetic algorithm-II (NSGA-II) and satisfaction-maximizing method are adopted to solve the multi-objective model with Pareto fronts and eclectic decision obtained. Finally, a case study is carried out to demonstrate that the approach can achieve economic and environmental aims and DR can help to accommodate the wind power.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Thermal Unit Energy Conservation Load Dispatch Based on Harmony Search Algorithm Combined with Dynamic Unit Commitment

The energy conservation generating scheduling is a kind of important work to insist in the power system that thermal power plants dominate. In the paper, a mathematical optimization model was built aiming at reducing the fuel consumption in the thermal units load dispatch. Considering the unit operating constraints, a method for energy saving load dispatch was presented, which combines the dynamic unit commitment strategy with the harmony search algorithm. The method was applied to the scheduling problem in a regional power system. The results show that the method proposed is feasible and plays a significant role in energy conservation in the thermal power distribution.

Fast computation of the maximum wind penetration based on frequency response in small isolated power systems

This paper investigates the frequency deviation and stability analysis of a small isolated power system in response to various load levels and demand penetration of unit generators. From the analysis an optimal generation unit commitment strategy is presented in terms of system operation and expansion. The proposed strategy aims to prevent unnecessary generation outages due to frequency disturbances in the system and to ensure system security as well as for the integration of wind energy. This paper also demonstrates that the proposed frequency response technique which is fast and simple can be used to calculate the maximum allowable wind penetration for a small isolated power system.

Second-order cone programming for solving unit commitment strategy of thermal generators

The short-term unit commitment (UC) problem of hydrothermal generation systems is a mixed-integer nonlinear programming (MINLP), which is difficult to solve efficiently, especially for large-scale instances. The perspective relaxation (PR) is an effective approach to constructing tight approximations to MINLP with semi-continuous variables. In this paper, the PR of UC problem is formulated as a mixed integer second-order cone programming (SOCP) model because the quadratic polynomial cost function of the UC problem is SOCP-representable. The proposed model is implemented by using the commercial optimization software IBM CPLEX 12.4. Extensive numerical studies have been conducted to verify the advantages of our proposed method. Instances of the test system vary from 10 to 1000 units. Our results indicate that the proposed method performs better than the existing methods in terms of production cost savings and faster computational times, especially for large systems.

Unit commitment strategy of thermal generators by using advanced fuzzy controlled binary particle swarm optimization algorithm

This paper presents a fuzzy controlled and multi-population based binary clustered particle swarm optimization (BCPSO) algorithm to solve short term thermal generation scheduling problem. In order to incorporate the uncertainties regarding forecasted load demand, the spinning reserve requirement and the total production cost, the formulations are modified by employing fuzzy logics. Each of these uncertain entities are associated with fuzzy membership functions which determine the degree of acceptance. The aggregated membership function, which combines the individual membership functions of fuzzified variables, is incorporated with the fitness value to provide the acceptability measurement of a particular candidate schedule. Typically, generation scheduling is a highly non-linear, multi-peak combinatorial optimization problem. In this method, the potential candidate schedules (or individuals) are distributed among several clusters based on their acceptance values. Each individual of a particular cluster then flies through to its cluster-space towards the cluster best while improving its personal best position. Gradually, as the population grows, the cluster space is also increased to ensure the global convergence. Therefore, this algorithm explores a larger search space and thus reduces the probability of local trapping. A dynamic probabilistic mutation operator is applied on the individual solutions based on their associated fitness values. Simulation result is provided to show the effectiveness of BCPSO while considering two different power system configurations.

Short-term electricity dispatch optimization of Ertan hydropower plant based on data by field tests

A short-term electricity dispatch optimization program required by the Ertan hydropower plant is developed to maximize hydropower production. Three field tests in various operating heads were carried out in the period of May 2009 to March 2010. Based on data of five test conditions, three operating zones for units in various operating heads were proposed. A short-term electricity dispatch optimization model was developed with physical and operational constraints. Unit commitment strategy was put forward for model solution, in which unit statuses and output statuses were classified. The strategy aimed at formulating better unit commitment plan according to forecasted load demand, ancillary service requirements, and initial operating status. The model and the strategy were verified by real cases. The results show that the optimal load distribution among units at every interval can be easily solved by the genetic algorithm based on a fixed unit commitment plan. Schedules are developed with higher average generation efficiency. Units can also be scheduled to operate for a less time within the rough zone and the second feasible zone. The proposed method is already operational for dispatch engineers of the Ertan hydropower plant to determine half-hourly schedules in one day.

Adaptive mixed-integer programming unit commitment strategy for determining the value of forecasting

This paper presents the development of a method to determine the value of forecasting (for load, wind power, etc.) in electricity-generation. An adaptive unit commitment (UC) strategy has been developed for this aim. An electricity generator faces demand with a given uncertainty. Forecasts are made to meet this load at the lowest cost. The adaptive UC strategy can be described as follows. Each hour, the generating company constructs a new forecast for a fixed number of hours. We assume that the first forecasted hour is in fact predicted correctly. For these forecasted hours, an optimal UC schedule is determined (given the on/off states of power plants for the current hour). The solution for the first hour (i.e., the one that was predicted correctly) is retained, and a new forecast is made. A 15,000 MW power system is used in a 168 hour (one-week) schedule. The UC problems presented in this work are solved through a Mixed-Integer Linear Programming (MILP) approach. In the first case, the effect of limited (correct) forecasting is investigated. Forecasts are made 100% correctly, but the UC scheme is built modularly and compared with the reference case, where the UC problem is solved for the one-week problem as a whole. Depending on the number of forecasted hours, solutions differ by up to 0.5% with the reference case. In a second case, when a certain error is imposed on the forecasts made (up to 5%), the deviations from the optimal solution become larger and amount in certain cases to almost 1%.

Operating reserve and reliability analysis of the Taiwan power system

A probabilistic approach to examining the effect of operating reserve on the reliability and security of the Taiwan power system is developed. To evaluate the probability of failure to carry load in system operation, an extension of the original PJM (Pennsylvania-New Jersey-Maryland) method is presented. System security is analysed using the reliable emergency output power (REOP) index. The proposed methods are applied to evaluate the reliability and security of the Taiwan power system using the operating records of the past five years. 32 load patterns are examined for each year to cover the different seasons of the year, the different days of the week and the peak and off-peak periods of the day. The factors affecting system reliability and security such as the amount and type of spinning reserves and the availability of rapid-start units are extensively examined. It is found that the presented probabilistic approach can provide valuable information which can assist the system operators to reach a secure and reliable, as well as economical, unit commitment strategy.

A fuel-constrained unit commitment method

A short-term fuel-constrained thermal unit commitment method which includes significant constraints such as take-or-pay gas contracts and limitations associated with the gas delivery system is described. The fuel constraints are explicitly considered in determining the short-term (e.g., daily, weekly) thermal unit commitment strategy. The method results from the extension of a novel iterative approach consisting of two phases: a sequential commitment phase and a parameter adjustment phase. The method is effective in both its computational requirements and its solution quality. Illustrative examples are presented. >

Wind Array Power Prediction for Improved Operating Economics and Reliability

A methodology for predicting wind power variations one or more hours ahead is developed. This prediction method is required for unit commitment and generation control strategies that have been developed to provide economic and reliable operation for utilities with large wind penetrations. The methodology utilizes a set of meteorological towers that encircle the wind turbine clusters at a radius of 100 miles. The prediction methodology must determine (1) the direction of propagation of the meteorological event, (2) the subset of meteorological towers that are in the direction of motion of the meteorological event and thus are to be used in the prediction, (3) the delays between the prediction sites in the wind turbine cluster and the selected subset of reference sites in the ring of meteorological towers to be used for prediction, and (4) the parameters of the predictive model that predicts wind speed based on weighted delayed wind speed measurements at the selected reference sites.