Unit Commitment Research Articles

Week-ahead dispatching of active distribution networks using hybrid energy storage systems

This paper presents a week-long scheduling approach to address the issues associated with uncertain stochastic generation. Specifically, the method is designed for active distribution networks (ADNs) hosting hybrid energy storages, composed by a hydrogen energy storage system (HESS) and a battery energy storage system (BESS). The inclusion of a pressurized HESS allows to balance energy over longer time periods, as opposed to methods considering only BESSs. To this end, this paper combines linearized models for the electricity grid with linearized models of the HESS to solve a tractable scheduling problem. The proposed optimal schedule consists of an active power trajectory at the grid connection point (GCP), called the dispatch plan, and the unit commitment schedule of a PEM fuel cell and electrolyzer system interfacing the electricity network with the HESS. Additionally, a bilevel model predictive control strategy is proposed, where the upper layer MPC computes a storage target accounting for the full horizon, while the lower layer computes the controllable resource setpoints to minimize the dispatch tracking error in each period. A numerical experiment shows the effectiveness of the proposed scheduling and control to accurately compute and track a dispatch plan over a full week. The results clearly show the benefits of combining a HESS with a BESS especially in periods where the prosumption is highly uncertain. Finally, we discuss the computational challenges associated with the weekly horizon and the use of a HESS that exhibits different dynamics than a BESS and propose an approach to mitigate the computational cost.

How wind forecast updates are balanced in coupled European intraday markets

AbstractThe market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future.

Chance Constrained Day Ahead Stochastic Unit Commitment with Multiple Uncertainties

Chance Constrained Day Ahead Stochastic Unit Commitment with Multiple Uncertainties

Investigating the Effects of Increased Thermal Generation by Unit Commitment Optimization in Hydrothermal Power Systems Using Lagrange Algorithm

Investigating the Effects of Increased Thermal Generation by Unit Commitment Optimization in Hydrothermal Power Systems Using Lagrange Algorithm

Multi-source coordinated low-carbon optimal dispatching for interconnected power systems considering carbon capture

The overall electricity consumption of electrolytic aluminum and ferroalloy loads is significant, and some of these loads have dispatch potential that can be used to locally absorb wind power while reducing dependence on conventional thermal power. To characterize the uncertainty of wind power, a fuzzy set of wind power forecasting error probability distribution based on the Wasserstein distance was first established, and the approximate radius of the fuzzy set was corrected under extreme scenarios. By introducing joint chance constraints, the inequalities of uncertain variables were established at the lowest confidence level to improve the reliability of the model. Next, a two-stage distributed robust optimal scheduling model for source-load coordination was developed. In the first stage, wind power forecasting information was fully utilized to schedule the electrolytic aluminum load and optimize unit commitment. In the second stage, the uncertainty of wind power output was considered to schedule the ferroalloy load and optimize unit output. The model was approximately transformed into a mixed-integer linear programming problem and solved using a sequential algorithm. The IEEE 24-bus system was used for case validation. The validation results show that the model can effectively improve wind power absorption capacity, reduce overall operating costs, and achieve a balance between low carbon emissions and robustness.

Investigating the Impact of Interconnection Line Power Losses on Unit Commitment of Grid-Connected Microgrids

Investigating the Impact of Interconnection Line Power Losses on Unit Commitment of Grid-Connected Microgrids

A novel green learning artificial intelligence model for regional electrical load prediction

Load prediction is crucial to unit commitment and scheduling in electricity systems; however, load prediction models consume considerable electricity. Most conventional prediction methods are based on deep learning (DL) models, such as long short-term memory and gated recurrent units. The nonlinear activation functions and backpropagation mechanisms used in these methods result in high computational complexity and correspondingly high energy consumption. This paper proposes a prediction model based on a green learning (GL) framework aimed at reducing energy consumption. The proposed GL model replaces the activation functions conventionally used for feature extraction with a hybrid scheme combining categorical and numerical features, such as seasonal climate features and multi-autoregression terms. The proposed GL model also eliminates the backpropagation methods used to optimize hyperparameters by employing seasonal centroids and the quantile auto-regression forest algorithm for classification/regression. In a case study, the proposed GL model significantly outperformed conventional DL models in terms of energy consumption without compromising accuracy. The energy savings of the proposed GL model are equivalent to the annual electricity consumption of 211 ∼ 565 households, increasing with model penetration. This paper demonstrates the importance of energy-saving models in mitigating the linkage effect of industrial carbon emissions. It also explores policy implications for the implementation of GL models.

Trade-off between frequency stability and renewable generation – Studying virtual inertia from solar PV and operating stability constraints

The significant increase in solar PV and wind capacity worldwide has raised growing concerns about power system frequency stability in several countries. One reason for this is that these renewable technologies offer almost no inertial response. However, there are some options available to address this issue. Renewables could now offer virtual inertia. Also, power system operators could impose frequency stability constraints as part of the generation Unit Commitment (UC). These options could potentially have a large impact on renewable generation and hence also on energy costs and system emissions. In this paper, we study two key options improving stability, inertia from solar PV generators supplied by virtual synchronous machines, and frequency stability constraints as part of the UC. We assess N-1 generation contingencies for the interconnected power systems of Chile, Argentina, and Peru. We found that the option of virtual inertia reduces the violation of the RoCoF in hours of high solar generation, without affecting renewable generation. On the other hand, frequency stability constraints ensure non-violation of all stability parameters but reduce renewable generation by 12 %. This reduction increases total operating costs by about 35 %, even when the constraints are combined with virtual inertia and battery support.

Integration of Electric Vehicle Power Supply Systems—Case Study Analysis of the Impact on a Selected Urban Network in Türkiye

Undoubtedly, the transition to electromobility with several million new, efficient charging points will have consequences for the energy industry, and in particular for network operators of the distribution infrastructure. At the same time, in the coming years the energy landscape will change into a system in which an increase in decentralized systems based on renewable energy sources will take over the leading function. This transformation process will further increase the complexity and overall pressure for change in energy systems over the next decade. In order to be able to ensure the energy supply and the reliable system operation of the grids in the future as well, communicative networking of generators, storage systems, electrical consumers and grid equipment is indispensable. This study aims to investigate the consequences of including electric vehicles in Istanbul’s power system using a unit commitment simulation model. The presented considerations analyze how uncertain and managed charging strategies affect the power system in terms of operating costs and renewable resources. The presented simulations indicate that, in economic terms, the use of a managed charging strategy saves 2.3%, reducing the total cost from USD 66.71 million to USD 65.18 million. The recipients of the presented research are both the demand and supply sides of the future energy transformation based on the idea of synergy of electromobility and renewable energy sources within the framework of the smart city idea.

Assessing power profile characteristics in solar PV-storage integrated electricity markets: A quantitative study

Integrating solar PV inverters and storage devices into the modern power grid generates multiple power profiles with varying magnitudes. The intermittent nature of PV necessitates installing storage devices to reduce unit commitment challenges and accommodate reserve power. This paper proposes various operational factors to determine the boundaries for individual and aggregated power profiles within a cluster of PV systems and storage units. The study utilizes dispatchability and operating reserve factors (primary and secondary) during storage discharge mode to extract dispatchable power and primary and secondary reserve power. Similarly, storage arbitrage and non-dispatchability factors extract arbitrage, non-dispatchable, and a portion of supplemental or tertiary reserve power during storage charging mode. An optimization model leveraging mixed-integer linear programming is formulated to minimize operational expenditures considering these hybrid PV-storage power profiles. Extrapolated electricity market rates for hybrid power profiles are projected using the prevailing prices of photovoltaic (PV) systems and storage technologies. This paper utilizes the proposed aggregated operational factors as constraints while extracting these power profiles for a cluster of PV-storages. The efficacy of the proposed optimization framework is tested on a modified IEEE 34-bus system by adding 10 PV-storage devices on 10 buses. The final quantitative values correspond to the percent penetration and arbitrage of these power profiles.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

Frequency security-constrained unit commitment with fast frequency support of DFIG-based wind power plants

The integration of inverter-based renewable energy sources into power grids presents a unique challenge: a reduction in grid inertia, leading to potential frequency stability issues. This paper introduces an innovative approach to address this challenge, focusing on the dynamic role of wind power plants equipped with dual-fed induction generators (DFIG). Unlike traditional methods that rely on fixed inertia control strategies and wind power droop coefficients, our method offers a dynamic solution tailored to the fluctuating nature of grid frequency dynamics. We propose a novel variable inertia support mechanism, coupled with an optimized power point tracking control and strategic reserve, to maximize the effective use of wind power reserves without waste. Additionally, we account for the inherent uncertainties in wind power generation, enhancing the frequency support capabilities of wind turbines through a fast frequency response strategy. A key feature of our approach is the application of an advanced piecewise linearization fitting method to establish a robust frequency nadir constraint. This is integrated into a two-stage robust frequency security-constrained unit commitment model, enabling optimal real-time decision making for frequency stability in wind power plants. The efficacy and practicality of our proposed method are verified through a detailed case study on a modified IEEE 39-bus system and a hardware-in-the-loop experiment. Compared with the traditional fixed parameter control method, the cost is reduced by approximately 0.42%, while the linearization error at frequency nadirs remains within the range of -4% to 1.5%.

Robust optimization dispatch for PV rich power systems considering demand response and energy storage systems

In recent years, the ever-rising penetration of distributed photovoltaics (PV) power has presented substantial challenges in power system dispatch due to its inherent randomness and unpredictability. To bridge this gap, this paper proposes a two-stage robust optimization method for power system security dispatch considering traditional generators as well as flexible resources, such as load demand response and energy storage systems. Specifically, a price-based demand response model is established to optimize the system’s load curve during a day. On this basis, a two-stage optimization problem for day-ahead and intra-day power system dispatch model is proposed. The dispatch objective is to minimize the overall cost in worst-case scenarios through properly scheduling unit commitment (UC) as well as flexible resources in each dispatch interval. Column and constraint generation (C&CG) algorithm is adopted for problem solving. The effectiveness of the proposed method is validated by case studies based on a modified 6-node system and a 24-node system. Simulation results indicate that through appropriately scheduling the energy storage system and load demand response, the proposed dispatch method can significantly reduce the total operation cost of a PV rich power system, which in turn facilitates the integration of PV power.

A hybrid quantum–classical algorithm for mixed-integer optimization in power systems

Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and Optimal Transmission Switching to verifying Neural Networks for power system applications. The main issue of these formulations is the computational complexity of the solution algorithms, as they are considered NP-Hard problems. Quantum computing has been tested as a potential solution towards reducing the computational burden imposed by these problems, providing promising results, motivating the can be used to speedup the solution of MILPs. In this work, we present a general framework for solving power system optimization problems with a Quantum Computer (QC), which leverages mathematical tools and QCs’ sampling ability to provide accelerated solutions. Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow. Specifically, using an accelerated version of Benders Decomposition , we split a given MILP into an Integer Master Problem and a linear Subproblem and solve it through a hybrid “quantum–classical” approach, getting the best of both worlds. We provide 2 use cases, and benchmark the developed framework against other classical and hybrid methodologies, to demonstrate the opportunities and challenges of hybrid quantum–classical algorithms for power system mixed integer optimization problems.

Decision-focused learning under decision dependent uncertainty for power systems with price-responsive demand

Contemporary power systems are experiencing a growing integration of flexible resources on the consumer side. Different from flexible demand that submits specific bids to energy markets, price-responsive demand (PRD) adjusts its power consumption without notice, simply based on the resulting electricity prices as well as internal priorities and limitations. In this paper, we demonstrate how a high penetration of PRD (the behavior of which is invisible to the operator during the day-ahead unit commitment stage) results in systematic inefficiency costs and formulate the so-termed decision-focused learning problem of learning to provide a demand forecast which, once fed as an input to the operator’s economic dispatch optimization problem, results in an efficient dispatch. Interestingly, the prescribed demand forecast affects the resulting prices, which in turn affect the actual demand realization, giving rise to a decision-dependent uncertainty. Motivated by the problem’s hard-to-evaluate objective function, we solve it using Bayesian optimization. The empirical evaluations demonstrate significant savings in the effective real-time system cost, compared to the current practice of using the default demand forecast. Moreover, the method is shown to achieve a system cost that is fairly close to the one achieved by a system that fully integrates PRD into the day-ahead process; but without requiring any change in the operator’s existing dispatch algorithm while avoiding all efforts necessary for the integration of flexible demand, which is a widely pursued field of ongoing research.

Use of Machine Learning Models to Warmstart Column Generation for Unit Commitment

The unit commitment problem is an important optimization problem in the energy industry used to compute the most economical operating schedules of power plants. Typically, this problem has to be solved repeatedly with different data but with the same problem structure. Machine learning techniques have been applied in this context to find primal feasible solutions. Dantzig-Wolfe decomposition with a column generation procedure is another approach that has been shown to be successful in solving the unit commitment problem to tight tolerance. We propose the use of machine learning models not to find primal feasible solutions directly but to generate initial dual values for the column generation procedure. Our numerical experiments compare machine learning–based methods for warmstarting the column generation procedure with three baselines: column prepopulation, the linear programming relaxation, and coldstart. The experiments reveal that the machine learning approaches are able to find both tight lower bounds and accurate primal feasible solutions in a shorter time compared with the baselines. Furthermore, these approaches scale well to handle large instances. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete.

Short-Circuit Ratio and Short-Term Voltage Security Constrained Unit Commitment in High Wind Power-Penetrated Power Systems

Short-Circuit Ratio and Short-Term Voltage Security Constrained Unit Commitment in High Wind Power-Penetrated Power Systems

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

Frequency Stability-Constrained Unit Commitment: Tight Approximation Using Bernstein Polynomials

Frequency Stability-Constrained Unit Commitment: Tight Approximation Using Bernstein Polynomials

Deep Reinforcement Learning-Assisted Convex Programming for AC Unit Commitment and Its Variants

Deep Reinforcement Learning-Assisted Convex Programming for AC Unit Commitment and Its Variants