Unit Commitment Research Articles

Multi-Type Reserve Collaborative Optimization for Gas-Power System Constrained Unit Commitment to Enhance Operational Flexibility

With the wide application of the gas-power system, gas-power coupling equipment such as gas turbines are gradually becoming widely used, and the problem of insufficient system reserve capacity needs to be solved. In order to improve the operational flexibility of the gas-power system, this paper combines the source side, the load side, and the energy storage side to propose a multi-type backup system, and constructs the source-load-storage multiple reserve capacity system of the gas-power system. Through the participation of gas turbine, steam turbine, interruptible load, and energy storage battery to provide reserve capacity, it can fully cope with the output fluctuation of the load side and source side and realize the coordinated operation of multiple resources to provide reserve capacity. In addition, the coordination of gas turbines and steam turbines can further improve the operation flexibility of the gas-power system. Through the example analysis, it was found that the proposed method reduced the total operating cost of the system by 10.6%.

Frequency-constrained unit commitment under uncertain PFR of energy storage systems

By increasing the penetration of low-inertia renewable energy systems (RESs) in the power systems, the grid frequency stability becomes even more challenging. It is necessary to emulate the inertia or primary frequency response (PFR) of RESs with power electronic converters (PECs) to have more accurate model. Therefore, frequency constrained unit commitments (FCUCs) are developed based on fully reliable RESs or energy storage systems (ESSs) contribution in frequency control. As, PEC-based resources are not guaranteed to work perfectly in an outage, any sensor, control, or drive system malfunction results in false detection or response. Consequently, if the FCUC relies on the fast, and perfect inertia response of RESs, the frequency may exceed from its standard ranges in the presence of malfunction. To avoid frequency instability, the imperfect PEC-based resource contribution is accounted, here. Thus, the malfunctioned units are removed from the frequency ancillary services. As malfunctioned units are unknown, a distributionally robust optimization is utilized. This paper extracts a robust FCUC model considering the uncertain PFR of PEC-based ESSs. The model proficiency is analyzed under IEEE RTS96 system. It is concluded that the proposed method prevents frequency instability by spending 6.38 % more cost than the FCUC model with perfect operation of converters. In addition, merits of the proposed model get more significance in higher penetration of malfunctioned units.

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution.

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

Convex hull pricing as a risk mitigation device in unit commitment

Convex hull pricing as a risk mitigation device in unit commitment

A dual-timescale dynamics model for evaluating long-term profiles of distributed energy resources under multi-market mechanisms

Distributed energy resources (DER), encompassing distributed renewable resources and distributed energy storage systems, are progressively emancipating themselves from reliance on subsidies and engaging in the realms of energy, green certificate, and capacity markets. However, the long-term profile of DERs under multiple market conditions remains uncertain, and traditional methods are inadequate in addressing the challenges posed by the non-linearity and non-convexity of various factors that affect investment and bidding behavior. Additionally, the varying time scales of clearing models across different markets require a quantitative approach that combines long and short-term scales to determine DER profitability under various critical boundaries. To address these challenges, this paper proposes a dual-timescale dynamics model. Firstly, the system dynamics (SD) method is employed to establish the interplay among various market entities, simulating capacity investment of generation units on a long-term scale. Secondly, the stochastic unit commitment (SUC) is modeled to verify the operating constraints of the system while simultaneously calculating the short-term market clearing results. These results are then fed back to the long-term simulation loop to evaluate the further profitability of DERs. Lastly, a price adjustment mechanism was devised to simulate the frequent occurrence of negative energy prices in high-proportion renewable energy systems, thereby enhancing the fidelity of simulated price data to real-world environments. Through a 20-year simulation, the proposed model quantitatively analyzes the impact of market mechanisms on DER profitability and the influence of DERs on market clearing results. The model has the potential to provide valuable analytical support for the design of DER-related market mechanisms and policy decisions.

Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning

As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this issue frequency constraints are being included in the scheduling process, which ensure a tolerable frequency deviation in case of any contingencies. In this paper, a method is proposed to integrate the non-linear frequency nadir constraint into the unit commitment problem, using machine learning. First, a synthetic training dataset is generated. Then two of the available classic machine learning methods, namely logistic regression and support vector machine, are proposed to predict the frequency nadir. To be able to compare the machine learning methods to traditional frequency constrained unit commitment approaches, simulations on the power system of La Palma island are carried out for both proposed methods as well as an analytical linearized formulation of the frequency nadir. Our results show that the unit commitment problem with a machine learning based frequency nadir constraint is solved considerably faster than with the analytical formulation, while still achieving an acceptable frequency response quality after outages.

A Flexibility-oriented robust transmission expansion planning approach under high renewable energy resource penetration

High penetrations of renewable energy sources incorporated into transmission systems pose a substantial challenge to transmission expansion planning operations due to both the strong uncertainty of these sources and their high variability. However, most current robust transmission expansion planning studies focus exclusively on the issue of uncertainty, and neglect to optimize system flexibility in an effort to cope with the high variability of these sources. The present work addresses this critical issue by proposing a flexibility-oriented robust transmission expansion planning method developed according to the unit commitment characteristics of coal-fired and gas-fired generation units that fully considers the short-term flexibility requirements of electric power systems while maintaining high robustness to the uncertainties of RES outputs in long-term transmission expansion planning problems. The proposed complex model with unit commitment constraints containing massive binary variables is solved by first reducing the number of binary variables via the application of clustering techniques to identify generator units with similar generation properties, and then decreasing the complexity of the model further by relaxing the integer variables in the unit-clustered model. Finally, we develop generalized column-and-constraint generation algorithms that can solve the clustered model and the relaxed simplified model with greatly enhanced efficiency. Comparisons of the numerical results obtained by the proposed approach with existing state-of-the-art methods when applied to a simple Garver 6-node system and a realistically-sized power system demonstrate that the proposed method produces optimal RTEP solutions that account for both the uncertainty and variability of RES outputs. Moreover, the total cost of the proposed approach is 2.22×106$ less than that of the other state-of-the-art methods considered, which is of great significance in guiding practical transmission expansion planning applications.

Determination of required instantaneous power reserve for power system inertia enhancement, based on the unit commitment

Determination of required instantaneous power reserve for power system inertia enhancement, based on the unit commitment

Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Grids

With increasing installation of wind and solar generation, conventional synchronous generators in power systems are gradually displaced resulting in a significant reduction in system inertia. Maintaining system frequency and rate-of-change-of-frequency (RoCoF) within acceptable ranges becomes more critical for the stability of a power system. This paper first discusses the impact of inter-area oscillations on the system rate-of-change-of-frequency (RoCoF) security; then, the limitations on locational RoCoF accounting for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G - 1$</tex-math></inline-formula> contingency stability are derived. To capture highest locational RoCoF during the oscillation, multiple measurement windows are introduced. By enforcing these frequency related constraints, a location based RoCoF constrained security constrained unit commitment (LRC-SCUC) model is proposed. Furthermore, an effective piecewise linearization (PWL) technique is employed to formulate a RoCoF linearization problem and linearize the nonlinear function representing the location based RoCoF constraints in SCUC. Case studies are carried on IEEE 24-bus system to demonstrate the effectiveness of proposed LRC-SCUC model. The results also show that deploying virtual inertia techniques not only reduces the total cost, but also improves the system market efficiency.

Ancillary Services and Risk Assessment of Networked Microgrids Using Digital Twin

The networked microgrids (NMGs) have become one of the better means of improving the resiliency and reliability of power system networks. NMGs need proper coordination of all microgrids in either grid-tied or isolated mode. This study proposes the use of a digital twin (DT) for the energy management of NMGs in the distribution system. The NMGs are virtually represented using a neural network (NN) by training the data from the different optimal results of unit commitment (UC). The UC formulation of NMGs considers risk assessment due to uncertainty of demand and renewable energy when the NMGs are operated in isolated mode and schedules ancillary service transactions when the NMGs are connected to the power grid. The NN predicts the schedule of diesel generators, fuel cell (FC) and the battery energy storage system (BESS) in both isolated and gird-tied modes. The merit of the proposed method is that the virtual model (NN) can interact with the physical model (NMGs) in a real-time manner that depends on its operational mode. Restated, the virtual model can provide real-time dispatch updates in the schedule of conventional generators, FC and BESS. A real-time digital simulator (Opal-RT eMegasim) was used to validate the proposed method by testing a modified CIGRE benchmark model and a modified IEEE 34-bus system.

Research on Regional Spot Market Clearing Model Considering Subregional Power Exchange Constraints

The goal of the power market is to optimize the allocation of power resources. The regional power spot market can expand the space of market optimization, which makes each sub-regional demand and resource sufficiently complemented, and it is an important part of the power spot market. The difficulty of clearing the regional spot market lies in the following two aspects. One is to take into account the different power market models of each sub-region, and the other is that the regional clearing results can ensure the safety of the power system in each sub-region. This article proposes a dual-layer model that contains the power exchange constraints of the sub-region. Its boundary conditions that are based on the unit commitment results of each sub-region clearing model are used for regional spot market clearing. It is compatible with the possible power spot market model of each sub-region, but also fully considers the clearance of the sub-region. At the same time, it ensures that the regional clearing results will not affect the safe operation of the system in each sub-region. The article verifies the validity of the design area through calculation, and analyzes the clearing effect of the regional market.

Benefit Evaluation of Hydrogen Production System Harnessing Curtailed Wind Considering Integrated Demand Response

With the continuous increase of wind power installed capacity in China in recent years, the problem of wind curtailment has become increasingly serious. Using curtailed wind power for hydrogen production is an effective way to reduce wind curtailment. In this paper, based on the complementary characteristics of various energies in the electric-hydrogen system, a capacity planning model of the energy system considering wind curtailment wind-to-hydrogen technology and integrated demand response is established. The proposed model utilizes a fast unit commitment technology, with the function of solving the optimum capacity of various equipment in the electrolytic hydrogen production system under different wind curtailment utilization rates and different wind power installed capacities. A case study based on Xinjiang province, which has rich wind energy resources, is conducted to obtain the optimal composition of new-built capacities of the electrolytic hydrogen production system. In light of the simulation results, the levelized cost of hydrogen is proportional to the wind power installed capacity and inversely proportional to the utilization rate of wind curtailment.

Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges

This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established by the IEEE Technology and Innovation Subcommittee to first review the state-of-the-art of the security-constrained unit commitment (SCUC) business model, its mathematical formulation, and solution techniques in solving electricity market clearing problems. The Task Force then investigated the emerging challenges of future market clearing problems and presented efforts in building benchmark mathematical and business models.

Short term load forecasting using evolutionary algorithm for Tajikistan

&lt;p&gt;Load forecasting is a significant element in the energy management system of power systems. Precise load forecasting aids electric utilities to conduct decisions of unit commitment, reduction of spinning reserve capacity, and schedule device maintenance plan. Furthermore, load forecasting contributes to reducing the generation cost, and it is fundamental to the reliability of the power systems. On the other hand, short-term load forecasting is substantial for economic running. The forecasting precision directly affects the reliability, economy running and supplying power quality of the power system. Hence, finding the required load forecasting method to enhance the accuracy is valuable for forecasting precision. This paper proposed particle swarm optimization (PSO) to improve working support vector machine (SVM), SVM regression model is derived; also derived SVM with PSO. Support vector machine (SVM) model is adopted with and without PSO based on the historical load data and meteorological data of Tajikistan country, analysis the various factors affecting the forecast. The historical data and the load forecasting factors to be considered are normalized. The two parameters of SVM significantly influenced the model, and therefore it optimized using evolutionary algorithm.&lt;/p&gt;

Fast Solution Method for the Large-Scale Unit Commitment Problem with Long-Term Storage

Fast Solution Method for the Large-Scale Unit Commitment Problem with Long-Term Storage

A two-stage stochastic unit commitment considering demand-side provider and wind power penetration from the ISO point of view

A two-stage stochastic unit commitment considering demand-side provider and wind power penetration from the ISO point of view

Daily scheduling of generating units with natural-gas market constraints

We address the daily scheduling of generating units (unit commitment) in a system whose production mix includes a large number of natural-gas-fired units. We assume that the natural-gas system, operating under market conditions, does its best to supply the natural-gas for electricity production required by the power system. We formulate this problem as a bi-level model whose upper-level problem represents the scheduling of generating units under a second-order conic relaxation of the AC network constraints, and whose lower-level one represents the clearing of the natural-gas market. The upper-level problem is mixed-integer second-order conic, while the lower-level one is second-order conic as well, but continuous. We replace the lower-level problem by its optimality conditions in the form of primal constraints, dual constraints, and strong duality equality, which results in a single-level nonconvex mixed-integer nonlinear optimization problem. To improve the computational tractability of such formulation, we derive a mixed-integer second-order conic relaxation of the single-level nonconvex mixed-integer nonlinear optimization problem. Such problem is solved using a Benders-type outer-approximation decomposition framework with a master mixed-integer linear problem and two subproblems that are nonlinear but convex. The communication between the master problem and the subproblems is enhanced by incorporating into the master problem linearized information pertaining to the subproblems and valid linear inequalities. The benefits of the proposed model and the effectiveness of the solution procedure are illustrated using the IEEE 24-bus test system coupled with a 20-node natural-gas system and the Central Illinois 200-bus power system coupled with a 48-node natural-gas system.

An improved binary African vultures optimization approach to solve the UC problem for power systems

Unit commitment (UC) is one of the most crucial problems in electrical power systems. It concerns finding the optimal schedule for generating units such that the production cost is minimized over a given period. This paper aims to solve the unit commitment problem by proposing a binary version of a recent metaheuristic algorithm, the African Vultures Optimization Algorithm (AVOA), which is a nature-inspired metaheuristic algorithm that mimics the hunting mechanism and behavior of African vultures. This algorithm has the advantage of creating various phase shift approaches to avoid premature convergence and local optimum trapping. AVOA uses two mechanisms for the exploration phase and four for the exploitation phase that ensure the algorithm's ability to diversify and intensify. The AVOA is a continuous algorithm that cannot tackle the mixed-integer nature of the UC. In this paper, a sigmoid transfer function is used to convert the algorithm into a binary algorithm to decide the on/off status of the generating units. To further enhance the performance of the proposed algorithm, new values of the controlling parameters are proposed. Finally, the performance of the proposed algorithm is tested on IEEE 30-, 14-, and 57-bus systems. The proposed Binary AVOA (BAVOA) has superiority over the other algorithms given in the case study. In addition, results revealed that the BAVOA gives better results in the discrete search space (DSS) compared to the continuous search space (CSS). For instance, the operation cost of an IEEE 30-bus system in the DSS is 12,768 dollars which is 7% less than the CSS.

Hydropower Unit Commitment Using a Genetic Algorithm with Dynamic Programming

This study presents a genetic algorithm integrated with dynamic programming to address the challenges of the hydropower unit commitment problem, which is a nonlinear, nonconvex, and discrete optimization, involving the hourly scheduling of generators in a hydropower system to maximize benefits and meet various constraints. The introduction of a progressive generating discharge allocation enhances the performance of dynamic programming in fitness evaluations, allowing for the fulfillment of various constraints, such as unit start-up times, shutdown/operating durations, and output ranges, thereby reducing complexity and improving the efficiency of the genetic algorithm. The application of the genetic algorithm with dynamic programming and progressive generating discharge allocation at the Manwan Hydropower Plant in Yunnan Province, China, showcases increased flexibility in outflow allocation, reducing spillages by 79%, and expanding high-efficiency zones by 43%.

An intraperiod arbitrary ramping-rate changing model in unit commitment

In traditional unit commitment models, the ramping process of coal-fired generators is often represented by either a static average ramping rate or a once-changing rate model. However, this approach fails to accurately capture the actual ramping process of the unit, resulting in scheduling biases, particularly in systems with renewable energy sources. To address this issue, we propose a dynamic piecewise linear ramping (DPWLR) model that allows arbitrary multi-changes of ramping rates within a single period. We shift our focus from slope changes to time changes. By using a series of location indicator variables that satisfy the type 2 special ordered set, we locate the forward and backward time change limits and then determine the up and down power output limits of the units at each hour. We tested the intraperiod multiple-changing DPWLR model, as part of the unit commitment model, on different systems, including a 3-unit system, a 10-unit system, the IEEE RTS-79, and a 100-unit system. Comparative analysis demonstrates the functionality and superior performance of our proposed model.