Unit Commitment Research Articles

Unit commitment with AC optimal power flow constraint

Because of the enormous economic gain it may offer, unit commitment (UC) has been a primary optimization job in the daily operation planning of contemporary power systems. The AC optimal power flow (ACOPF) constraint is a method for determining the ideal load arrangement in alternating current (AC) power systems. Using unit commitment and ACOPF limitations to determine optimal load distribution is an important approach to ensure the steady functioning of power systems. The unit commitment approach with ACOPF constraint is examined in this study. The aims and contents of the study are set by analyzing the current approaches, and a piecewise linearized unit commitment method is developed that takes into consideration the ACOPF limitations and has good accuracy and practicality. Finally, to validate the method’s effectiveness, it was tested using a six-bus test system.

Renewable Energy Power System Unit Commitment Considering Regional Frequency Stability Requirement

With the development of new power systems, the formation of a “double high” pattern in frequency has made frequency security a prominent problem. It is particularly important to formulate a unit power generation plan with frequency security constraints at the day-ahead scale. However, a unit commitment model with frequency security constraints based on a unified frequency for the entire network cannot be used because the frequency exhibits spatial variations. This problem is solved in this study by establishing a single-machine aggregation model for the frequency response of a partitioned power system. In the unit commitment model, the frequency support between individual regions is established based on the distribution of the disturbance power over individual subregions. The index of the initial frequency change rate is introduced for each subregion to establish an analytical expression for the relationship between the frequency security constraints and the unit commitment scheme. Finally, the effectiveness of the model and the solution algorithm is verified for the improved IEEE39 node system.

Inter-regional unit commitment and PV scheduling considering frequency constraints

Unit commitment problem considering renewable uncertainty has received a lot of attention in recent years. The high-level penetration of renewable lowers the system inertia, posing challenges to frequency stability. This work proposes a unit commitment model considering frequency limits. The High Voltage Direct Current (HVDC) tie-line is modeled to transfer PV generation to load centers while providing frequency support. A multi-machine system frequency response model is employed to approximate frequency excursion. PV is considered semi-controllable in the scheduling model. A novel solution approach is proposed to solve the unit commitment problem with nonlinear frequency stability constraints. Case studies demonstrate the benefits and effectiveness of the proposed approach.

Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation

Decarbonisation is driving dramatic growth in renewable power generation. This increases uncertainty in the load to be served by power plants and makes their efficient scheduling, known as the unit commitment (UC) problem, more difficult. UC is solved in practice by mixed-integer programming (MIP) methods; however, there is growing interest in emerging data-driven methods including reinforcement learning (RL). In this paper, we extensively test two MIP (deterministic and stochastic) and two RL (model-free and with lookahead) scheduling methods over a large set of test days and problem sizes, for the first time comparing the state-of-the-art of these two approaches on a level playing field. We find that deterministic and stochastic MIP consistently produce lower-cost UC schedules than RL, exhibiting better reliability and scalability with problem size. Average operating costs of RL are more than 2 times larger than stochastic MIP for a 50-generator test case, while the cost is 13 times larger in the worst instance. However, the key strength of RL is the ability to produce solutions practically instantly, irrespective of problem size. We leverage this advantage to produce various initial solutions for warm starting concurrent stochastic MIP solves. By producing several near-optimal solutions simultaneously and then evaluating them using Monte Carlo methods, the differences between the true cost function and the discrete approximation required to formulate the MIP are exploited. The resulting hybrid technique outperforms both the RL and MIP methods individually, reducing total operating costs by 0.3% on average.

Assessment of the required running capacity in weakly interconnected insular power systems

In this paper a methodology is proposed for assessing the required running capacity in weakly interconnected insular power systems. This information is of crucial importance for system operators, when deciding the operation policy of the interconnected power system. In the proposed methodology, a probabilistic analysis is initially employed to identify probable extreme stress conditions of the local oil-fired units of the insular power system and the interconnector. Then, based on the unit commitment guidelines and the technical limitations of the interconnector, a power flow analysis is proposed to be followed. Hence, useful insights can be obtained for the system operators, regarding the required running capacity and the interconnection operation. To validate the proposed methodology’s efficacy, the case of the interconnection of Crete with the Hellenic Transmission System is considered. The presented case study follows the recently proposed guidelines for unit commitment in the power system of Crete following to the interconnection, while considering the extreme scenarios derived from the probabilistic analysis. The existing oil-fired units that will be required to remain available for dispatching after the operation of the AC interconnector are identified, while the interconnection operation is characterized.

Automatic definition of topological corrective actions for post-contingency OPEX minimization

This paper presents a practical algorithm for the automatic definition of topological corrective actions (TCAs) to minimize the additional operating expenses required to mitigate the adverse effects of contingencies in the network. Contingency and sensitivity analyses are used to identify possible TCAs that comprise the disconnection of single branches, pre-defined switching actions on one or more circuit breakers (CB), and changing the tap position of phase-shifter transformers. A novel method is proposed for the identification of possible CB closing TCAs. For the TCAs that do not lead to uncontrolled cascade tripping, unit commitment (UC) calculations are eventually run to obtain the economically optimal amount of conventional generation redispatch, renewable energy curtailment and load shedding required to eliminate the residual overloadings after each possible TCA is applied. The proposed approach does not reformulate the UC optimal power flow problem but rather uses standard calculation functions available in industrial-grade software. The effectiveness of the proposed approach is shown for a reduced-order transmission system prepared to capture many post-contingency conditions found in practice.

Unit commitment of integrated energy system considering conditional value-at-risk and P2G

Unit commitment of integrated energy system considering conditional value-at-risk and P2G

Optimisation of generation unit commitment and network topology with the dynamic thermal rating system considering N-1 reliability

The electrical grid is a vital component of modern society that requires significant and cost-effective investments to ensure its reliability. The dynamic thermal rating system (DTR) and network topology optimization (NTO) aim to increase the capacity of existing networks and provide flexible options for enhancing power system reliability. The co-optimization framework for generation unit commitment and network switching is formulated with for N-1 reliability, a gap that has not been previously addressed. This is important as it ensures: (1) economic efficiency in power system despatch, (2) optimal operation of both generation and transmission systems, and (3) power grid operation that can withstand the loss of any single component without causing a cascading failure. The reliability impacts of these new proposed operating strategies are also tested on complex, large-scale power systems under long-term, multi-area weather conditions. This proposed work is useful for power system planners, as it helps them identify ways to improve the reliability and efficiency of power systems under various weather conditions. This paper shows that using the DTR-NTO combination improves EENS by 50% in both test cases compared to the original condition. Additionally, it also makes the reliability assessment more realistic when taking into account multi-area weather conditions.

Unit Combination Scheduling Method Considering System Frequency Dynamic Constraints under High Wind Power Share

Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems.

Wasserstein‐metric‐based distributionally robust optimization method for unit commitment considering wind turbine uncertainty

AbstractThe penetration of wind turbines in the power grid is increasing rapidly. Still, the wind turbine output power has uncertainty, leading to poor grid reliability, affecting the grid's dispatching plan, and increasing the total cost. Thus, a distributionally robust optimization method for thermal power unit commitment considering the uncertainty of wind power is proposed. For this method, energy storage and interruptible load are added to simulate increasingly complex electricity consumption scenarios. Furthermore, the amount of load cutting reflects the satisfaction level of electricity consumption on the user side. Based on Wasserstein metric, an ambiguity set is established to reflect the probabilistic distribution information of the wind power uncertainty. An ambiguity set preprocessing method is proposed to depict the probability distribution of ambiguity set more clearly, to minimize the operation cost under the condition that the uncertainty of wind turbine output power obeys the extreme probabilistic distribution of the ambiguity set. The test case in a modified version of the IEEE 6‐bus system shows that the proposed method can flexibly adjust the robustness and economy of optimization decisions by controlling the sample size and the confidence of Wasserstein ambiguity set radius. In addition, the proposed ambiguity set preprocessing method can obtain more economical dispatching decisions with a smaller sample size.

A fast linearized AC power flow-constrained robust unit commitment approach with customized redundant constraint identification method

The large-scale integration of renewable energy resources in the power system challenges its economic and secure operation. Particularly, the increasing penetration of renewable energy will result in insufficient system voltage regulation and reactive power support capabilities, and may cause high risks of nodal voltage and branch flow violations. Therefore, to hedge the operational risks under the worst realization of uncertainties of renewable energy sources, a two-stage robust unit commitment (UC) model is developed. Meanwhile, the convexified AC power flow model is incorporated in the robust UC model to more accurately characterize the real-time operating status of power systems. On this basis, an AC power flow-constrained robust unit commitment (ACRUC) model is formulated. A circular linearization method is then adopted to handle the quadratic constraints in the original AC power flow model, transforming them into tractable linear constraints. Furthermore, to reduce the computational complexity caused by the large-scale newly-added constraints after the linearization process, a customized redundant constraint identification (RCI) method is developed, in which two different modes (i.e., cold and warm start modes) are designed considering the difference in base case system operating condition for linearizing branch losses. Then, the redundant network security constraints could be identified by solving a series of relatively simple optimization subproblems. Numerical results on the modified NERL-118 test system indicate that the proposed model could accurately depict actual operation and scheduling conditions, and also verify that the proposed customized RCI method could effectively reduce the problem scale and improve the solution efficiency.

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems.

Stochastic distributionally robust unit commitment with deep scenario clustering

The increasing penetration of intermittent renewable generation in power systems allows for more available historical data at hand. However, it is challenging for traditional distributionally robust optimization to capture the difference and heterogeneity in historical samples which makes the solution remain highly conservative. To comprehensively characterize the underlying factors of uncertainty, we proposed a data-driven stochastic distributionally robust optimization model for unit commitment via group-wise ambiguity set constructed by deep representation clustering method. The expectation of the worst-case distribution under a group of scenarios is calculated in the model, with assuming that the scenarios belong to different groups which are ambiguity. A tractable approximation of the model is derived to avoid computational burden and we analyze the optimality conditions of the approximate formulation. Simulations on a modified IEEE-118 illustrate that the proposed approach can make a trade-off between stochastic optimization and distributionally robust optimization and has benefits in reducing the operation costs with the capability to hedge against the perturbation of unrelated samples.

Short-term electric load forecasting using particle swarm optimization-based convolutional neural network

Short-term electric load forecasting is essential for the operation of power systems and the power market, including economic dispatch, unit commitment, peak load shaving, load management and bidding strategy development. This paper presents a novel method that uses a hybrid convolutional neural network (CNN) that is cascaded with a fully-connected network, to conduct 24h-ahead electric load forecasting in power systems. Spearman’s rank-order correlation, which serves as input size information for the CNN, was used to measure the cross-correlation between two sets of observations (chronological loads) of time shifts or lags. Both max and average pooling operations were utilized to extract data features in concatenation while spatial and conventional dropouts were employed to avoid overfitting in the hybrid CNN. The proposed method used two loops to yield the optimal CNN. The outer loop optimizes the structure and hyperparameters (such as kernel size) of the hybrid CNN by a proposed method of enhanced elite-based particle swarm optimization (EEPSO) while the inner loop optimizes the parameters (such as values of a kernel) and weights in neural networks using the Adam optimizer. The EEPSO is based on the mean search and chaotic descending inertia weight. The proposed method was used to study the 2018 and 2019 hourly load data in Taiwan Power Company. Simulation results show that the proposed method outperforms the well-known ARIMA, Radial Basis Function Network, Support Vector Regression, Long Short-term Memory and their hybrid variants.

Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

Accurate wind power forecasting plays a crucial role in the planning of unit commitments, maintenance scheduling, and maximizing profits for power traders. Uncertainty and changes in wind speeds pose challenges to the integration of wind power into the power system. Therefore, the reliable prediction of wind power output is a complex task with significant implications for the efficient operation of electricity grids. Developing effective and precise wind power prediction systems is essential for the cost-efficient operation and maintenance of modern wind turbines. This article focuses on the development of a very-short-term forecasting model using machine learning algorithms. The forecasting model is evaluated using LightGBM, random forest, CatBoost, and XGBoost machine learning algorithms with 16 selected parameters from the wind energy system. The performance of the machine learning-based wind energy forecasting is assessed using metrics such as mean absolute error (MAE), mean-squared error (MSE), root-mean-squared error (RMSE), and R-squared. The results indicate that the random forest algorithm performs well during training, while the CatBoost algorithm demonstrates superior performance, with an RMSE of 13.84 for the test set, as determined by 10-fold cross-validation.

A Space Reduction Heuristic for Thermal Unit Commitment Considering Ramp Constraints and Large-Scale Generation Systems

This paper expands the research around a recently proposed method to reduce the search space region for thermal unit commitment problems. The importance of such techniques comes from the combinatorial explosion regarding the variables of the problem when there are a large quantity of generating units in the system. The proposed heuristic approach utilizes sensitivity indices to gather information about the system and fix many of the binary decision variables over the planning horizon. This work further explores the method by demonstrating its effectiveness in large-scale systems subjected to ramp constraints. Despite the significantly increased complexity, the results of this paper indicate that the method can achieve high quality solutions notably faster than other approaches from the literature.

Optimization of integrated energy system considering transmission and distribution network interconnection and energy transmission dynamic characteristics

The traditional hierarchical scheduling and operational model of transmission and distribution networks has failed to fully utilize the adjustable resources of the entire grid, consequently limiting the integration of renewable energy. To fully excavate the potential of transmission and distribution (T&D) coordination in enhancing the level of renewable energy assimilation and operational efficiency, in this paper, transmission grid and distribution network in the framework of synergetic unit commitment (TDS-UC) considering the dynamic characteristics of the electricity-gas-heat integrated energy system is proposed. The framework developed in this paper allows for the integration of multiple energy sources, including electricity, gas, and heat, into the dispatch strategy of the TG. To improve scheduling flexibility, the dynamic characteristics of the natural gas and heating networks are also considered in the TDS-UC model. A coordinated interactive optimization model for T&D is formulated, taking into account the multi-energy coupling relationship of the IES. To solve the TDS-UC model, a decentralized analytical target cascading algorithm is used to obtain an independent TG optimization problem and multiple ADNs local optimization problems that can be solved in parallel. The proposed model is transformed into an easy-to-solve mixed-integer linear programming (MILP) problem using an improved one-dimensional approximation piecewise linearization method to deal with the non-convexity nonlinear natural gas Weymouth equation. Numerical experiments are conducted on both a T6D2 and a T118D10 test system to demonstrate the efficiency and practicality of the proposed methodology.© 2017 Elsevier Inc. All rights reserved.

Integrated Renewable Photovoltaic/Thermal and Non-renewable Combined Heat and Power Unit Commitment Considering Multiple Energy Storage Systems

Integrated Renewable Photovoltaic/Thermal and Non-renewable Combined Heat and Power Unit Commitment Considering Multiple Energy Storage Systems

Stochastic Unit Commitment Study in a Power System with Flexible Load in Presence of High Penetration Renewable Farms

In this paper, a new hybrid multiobjective algorithm, namely, the modified bald eagle search Algorithm (MBES), integrated with the grasshopper optimization algorithm, is proposed to solve the unit commitment (UC) problem. We consider a standard 10-unit power system with two wind farms, two photovoltaic farms, and flexible loads for optimization purposes. The UC problem is tackled under uncertainties related to demand and renewable generation capacities. To account for these uncertainties, probability density functions (PDFs) are assigned to the sources of uncertainty, and Monte Carlo simulation (MCS) is employed to select several scenarios with specific probability coefficients. Additionally, two innovative objective functions based on operation cost and emissions are introduced, with each scenario weighted based on its occurrence probability. To assess the performance of the proposed MOGOA-MBES algorithm, simulations are conducted across three scenarios with varying conditions, and the results are compared against those obtained from several multiobjective algorithms. Our findings, supported by optimization results and the S-metric index, demonstrate that the proposed MOGOA-MBES algorithm outperforms other algorithms in terms of reducing operation cost and emissions. Furthermore, the simulation results reveal that uncertainties lead to an increase in cost and emissions, whereas the inclusion of flexible loads and their participation in the UC program can effectively mitigate cost and emission levels.

Solving wind‐integrated unit commitment problem by a modified African vultures optimization algorithm

AbstractUnit commitment (UC) stands out as a significant challenge in electrical power systems. With the rapid growth in power demand and the pressing issues of fossil fuel scarcity and global warming, it has become crucial to enhance the utilization of renewable energy sources. This study focuses on addressing the UC problem by incorporating a wind farm and proposes a modified version of the metaheuristic African vultures optimization algorithm (AVOA) in binary form, utilizing the sigmoid transfer function. The modified AVOA employs multiple phase‐shift tactics to overcome premature local optima. By determining the on/off status of generating units, the modified AVOA improves the algorithm's effectiveness. Additionally, the paper develops an auto‐regressive moving average model (ARMA) to forecast wind speeds, with the AVOA assisting in selecting the optimal orders (q and p) of the ARMA model. This is done using historical wind speed data to capture uncertainty in the wind speed. The wind power is then calculated using various models and integrated into the UC problem. The effectiveness of the modified AVOA is examined on the IEEE 30‐bus system. The binary AVOA (BAVOA) outperforms several algorithms presented in the case study, demonstrating its superiority. Furthermore, the results indicate that BAVOA delivers superior outcomes within the discrete search space when compared to the continuous search space.