Unit Commitment Model Research Articles

Optimality-feasibility-aware multistage unit commitment considering nonanticipative realization of uncertainty

The intrinsic uncertainty in source-load power and line outage poses huge obstacles to unit commitment (UC) optimization in power grids. To satisfy the practical requests in power grids, this paper exploits a multistage robust UC method to promise both optimality and feasibility in nonanticipative scheduling. Firstly, a multistage robust UC model is established accounting for the sequential realization of uncertainty. This model makes decisions with the goals of economy in the normal condition and power balance in the emergent condition. Secondly, a customized multi-cut decomposition algorithm is offered to address the intractable multistage robust optimization problem. The original multistage problem is decoupled into the form of one master and several slave problems (MP-SPs), where the robust dual dynamic programming (RDDP) solves the resulting multistage max–min SPs, and the column-and-constraint generation (C&CG) algorithm successively constructs multiple cutting planes to ensure the optimality and convergence of the alternative optimizations between the MP and SPs. Computational tests on real-size power grids validate the practicability and superiority of the proposed multistage robust UC method, which is of great significance in guiding system operations.

Innovative Unit Commitment Modeling Approach Considering Multi-infeed VSC-HVDC Links

This paper introduces an improved optimization model for the unit commitment (UC) problem of AC power systems considering multi-infeed VSC-HVDC links. The proposed formulation is based on the linear modeling of the AC network and point-to-point HVDC links, where power losses are properly considered by piecewise linearization of AC lines, DC links and VSC units. This UC model is featured by a mixed integer linear programming (MILP) framework, thus enabling effective calculations of the optimal hourly generation scheduling for multi-infeed VSC-HVDC power grids. These sophisticated power grid studies are associated with day-ahead electricity markets carried out by power system engineers at control centers. To demonstrate the applicability of the proposed MILP-based UC model, the IEEE RTS 24-bus test system is used to carry out two case studies. The first incorporates three point-to-point HVDC links where an in-depth analysis is carried by comparing its outcomes, with and without HVDC links. The second case study features two interconnected areas having three-infeed HVDC links and several tens of generation units. Both systems are analyzed for a 24-hr planning horizon. It is confirmed that the UC model described in this paper permits to study practical power grids with multi-infeed VSC-HVDC links.

A linear AC unit commitment formulation: An application of data-driven linear power flow model

Unit commitment is an important schedule procedure in power system daily operations, and an ideal version is to combine the unit commitment with the optimal power flow, thereby, it achieves an AC-power-flow-constrained unit commitment problem, which is referred to as the AC unit commitment. The AC unit commitment is quite benefit for system operation, however, the AC unit commitment problem is intractable since it is generally a large-scale non-convex and nonlinear program.To overcome this difficulty, this paper proposes a linear AC unit commitment model, which achieves a high-accuracy solution that is close to the solution of the exact AC unit commitment, while it keeps the problem from getting into a huge computational burden. To achieve a high accuracy, the recent data-driven linear power flow model is adopted, and for making the data-driven linear power flow model applicable for the unit commitment problem, a chance-constrained support vector regression approach is proposed to the replace the common least-squares regression in former data-driven linear power flow models, and physical models are used to deduce aid formulations to make the data-driven linear power flow model more robust.The resultant linear unit commitment model is tested on several standard systems, including a 2000-bus system, and the results verify the effectiveness of the proposed method, and show that the accuracy of the proposed data-driven linear power flow model improved about two-folds compared with the classical one.

Coordinated integration of distributed energy resources in unit commitment

Distributed energy resources, such as electric vehicles and roof-top solar panels have become increasingly popular and essential components of power distribution systems. However, their growth poses significant challenges to system operation, such as bidirectional power flow, intermittent power generation, increased peak demand, and unexpected frequency/voltage fluctuation. To tackle these challenges, we developed a vehicle-to-grid model, which incorporated dynamic electric vehicles usages, such as driving times, driving distance, and charging/discharging locations. The machine learning method of principal component analysis and XGBoost were used to develop the solar energy prediction model. We also developed a novel unit commitment model to coordinate and aggregate the distributed energy resources in power generation economic dispatch. Electric vehicle charging was used as elastic demand and electric vehicle discharging was used as power generation sources. The solar energy was considered as a negative load. Simulation results showed that uncontrolled electric vehicle charging and solar energy could negatively impact the power systems. For example, the load ramping rate was increased by 384%. Simulation results also showed that the proposed models could mitigate the negative impact and improve energy efficiency. For example, the peak demand, load ramping rate and average generation cost were reduced by 21%, 79%, and 27%.

Boosting Transmission System Flexibility in Network-Constrained Unit Commitment by Incorporating Distributed Series Reactors

In the recent decade, renewable energy sources (RES) such as wind and photovoltaic (PV) power generations gained more attention. However, despite their proven role in the reduction of operating costs of the network, their integration has some serious challenges such as the inherent uncertainties and the energy balance at the power grid. Some options to face these challenges, as well as to enhance the network’s flexibility, are demand response (DR), distributed FACTS (D-FACTS) devices, and energy storage systems (ESS). This paper focuses on the solution of the unit commitment (UC) problem in such a way that in addition to increasing the transmission network’s flexibility, the operation cost of the network decreases. To do this, distributed series reactors (DSRs) are employed as promising D-FACTS devices to enhance the transmission network’s flexibility. The uncertainty of RES is handled by the scenario-based stochastic programming technique. A mixed-integer linear programming (MILP) approach is developed, with a guaranteed global optimal solution, by developing a convex model for power flow in the presence of DSRs. The proposed UC model is implemented on the IEEE RTS 24-bus system, and the obtained results in different cases show the ability of the abovementioned flexibilities to reduce the operation cost of the network.

A Partnership of Virtual Power Plant in Day-Ahead Energy and Reserve Markets Based on Linearized AC Network-Constrained Unit Commitment Model

This paper presents coordinated energy management as a virtual power plant (VPP) framework with a wind farm, a storage system, and a demand response program in the transmission network according to the cooperation of VPPs in day-ahead energy and reserve markets. This strategy is based on a bilevel method, where it maximizes the expected VPP revenue in the proposed markets subject to constraints of renewable and flexible sources and the VPP reserve model in the upper-level problem. Also, a market-clearing model based on network-constrained unit commitment (NCUC) is explained in the lower-level problem so that it minimizes the expected operating cost of generation units constrained to a linearized AC-NCUC model. The scenario-based stochastic programming (SBSP) models the uncertainties of loads and WF power generation. Then, the master/slave decomposition method solves the bilevel problem to achieve an optimal solution at a low computational time. Also, since the lower-level problem is mixed-integer linear programming, the Benders decomposition algorithm is adopted to solve this problem. Finally, the suggested approach is implemented on IEEE test networks in GAMS software, and numerical results confirm the efficiency of the coordinated VPP management in DA energy and reserve markets and its capabilities in improving network operation.

A unit commitment and economic dispatch model of the GB electricity market – Formulation and application to hydro pumped storage

We present a calibrated unit commitment dispatch model of the GB electricity market applied to an economic analysis of the four existing hydro pumped storage (PS) stations in GB. More variable renewable electricity (VRE) increases PS profit by an amount dependent on the generation mix. With a large share of coal, a 1 percentage point (p.p) increase in VRE increases total PS profit by an average of 2.3 p.p. With a more flexible system, to achieve a similar profitability, the share of wind and solar should rise to more than 60% of supply. Inflexible coal with high VRE increases price volatility that drives PS arbitrage revenue. Higher system flexibility smooths VRE variability and limits PS price arbitrage, increasing the role of PS balancing and ancillary service revenue. Between 2015 and 22 PS stations were inactive in managing transmission constraints but provided 18% of fast reserve, response and other reserve services in 2022. Stacking up the modelled revenue from price arbitrage and 2022 balancing and ancillary services revenues against the ongoing fixed costs suggests that the four existing PS stations are quite profitable. However, the revenues will not be enough to cover capex and opex of a new 600 MW PS station without more balancing and ancillary services market opportunities.

Robust Unit Commitment of Integrated Electric-Heat Systems With Weather Parameter Driven Uncertainties

The integrated electric-heat system (IEHS) is currently one of the most prevalent realizations of a multienergy system, however, most of the existing research merely modeled the source, network, and demand sides of IEHS independently, without further exploring the internal correlation built up by the regional weather parameters. This article proposed a two-stage weather-driven risk-constrained robust unit commitment (WRRUC) model. Compared with the existing RUC works for IEHSs, two main contributions are made in our work: 1) a unified weather-driven uncertainty modeling method is proposed for the IEHS, which could decrease the conservativeness of the robust decision-making framework; 2) the uncertainty set boundaries are set as adaptive variables and the aftermath of unmodeled uncertainties is quantified by additional operational risk term in the objective function. The proposed WRRUC model is solved by the column-and-constraint generation algorithm. Compared with other off-the-shelf UC models, the summation of operational costs and risks of the test system is remarkably lower than the RUC case, verifying the effectiveness of the proposed weather-driven unified modeling of uncertainties and adaptive robust dispatch framework.

Quantum Distributed Unit Commitment: An Application in Microgrids

The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment problem complexity is expected to increase in the future because of the trend toward the increase of penetration of intermittent renewables. Even though quantum computing has proven effective for solving a host of problems, its applications for power systems’ problems have been rather limited. In this paper, a quantum unit commitment is innovatively formulated and the quantum version of the decomposition and coordination alternate direction method of multipliers (ADMM) is established. The above is achieved by devising quantum algorithms and by exploiting the superposition and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible solutions. The main contributions of this paper include: 1) the innovative development of a quantum model for Unit Commitment; 2) development of decomposition and coordination-supported framework which paves the way for the utilization of limited quantum resources to potentially solve the large-scale discrete optimization problems; 3) devising the novel quantum distributed unit commitment (QDUC) to solve the problem in a larger scale than currently available quantum computers are capable of solving. The QDUC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing.

Pricing Information in Smart Grids: A Quality-Based Data Valuation Paradigm

With the development of Internet technology, information has become an increasingly important asset for the operation and planning of power systems. However, the existing studies and practices pay little attention to information value evaluation, i.e., the potential for information to be converted into actual economic benefits. To this end, this paper designs an information market framework and proposes a generalized information valuation model to help price data in smart grids efficiently. Here we analyze the information value of photovoltaic (PV)-related data in a power system operation problem. Specifically, we examine how additional meteorological and PV power data help to improve day-ahead forecasting accuracy, thus enhancing unit commitment (UC). In this paper, information quality is captured by two indices of a set of PV-related data, i.e., Shannon entropy and non-noise ratio. Then a neural network-based engine is employed to predict day-ahead hourly solar power on the premise of input datasets with different information quality. Here we define forecasting accuracy as information utility, and discover an exponential relationship between such utility and information quality. Finally, a two-stage stochastic UC model is formulated to quantify the contributions of different PV-related datasets, in which real-time solar power deviation is penalized. In this instance, the economic value of PV-related data is measured as the operational cost reduction induced by forecasting accuracy improvement, which we find can be estimated by information quality. Case studies based on the IEEE 30- and 118-bus systems validate the effectiveness of the proposed paradigm and method.

A Unit Commitment Model Considering Feasibility of Operating Reserves under Stochastic Optimization Framework

Grid integration of renewable resources such as solar and wind energy can significantly raise the level of uncertainty in power systems, making the scheduled operation of generating units difficult. Therefore, the importance of operating reserves is more emphasized to prevent disruption by sudden changes in outputs of generators. In this paper, a stochastic unit commitment (UC) model to reflect uncertainty due to a large amount of renewable resources is proposed, in which upward and downward operating reserves are deployed simultaneously, and feasibility of the reserves is examined to make the deployed reserves supplied reliably. Uncertain parameters considered in the model are wind power availability, solar direct normal irradiance, and electric load. Two-stage stochastic programming is applied to the mathematical formulation, where UC decisions including dispatch are modeled as non-anticipative variables at the first stage, and redispatch decisions to serve realized electric demand are made at the second stage as recourse. By solving the UC problem, feasible and reliable stochastic UC and dispatch solutions can be provided to power system operators.

Solving stochastic hydro unit commitment using benders decomposition and modified stochastic dual dynamic programming

This paper proposes a stochastic hydro unit commitment (SHUC) model for a price-taker hydropower producer in a liberalized market. The objective is to maximize the total revenue of the hydropower producer, including the immediate revenue, future revenue (i.e., opportunity cost), and startup and shutdown cost. The market price uncertainty is taken into account through the scenario tree. The solution of the model is a challenging task due to its non-convex and high-dimensional characteristics. A solution method based on the Benders Decomposition (BD) and Modified Stochastic Dual Dynamic Programming (MSDDP) is proposed to solve the problem efficiently. Firstly, the BD is applied to decompose the original problem into a Benders master problem representing the hydro unit commitment and a Benders subproblem representing the optimal operation of the hydropower plants. The Benders subproblem, which contains a large number of integer variables, is further decomposed by the period and solved by the MSDDP proposed in this paper. Finally, we verify the effectiveness of the SHUC model and the performance of the proposed solution method in case studies.

Batteries and interconnection: Competing or complementary roles in the decarbonisation of the European power system?

Significant increases in renewable energy are needed in electricity systems in order to reduce greenhouse gas emissions, particularly in the face of increasing electricity demand arising from the electrification of heat and transport. Greater flexibility is required in the electricity system to facilitate this, using proven flexibility providers such as batteries and interconnection or new technologies yet to be proven. This paper investigates how the relationship between battery and interconnection development and carbon price can impact carbon dioxide emissions and renewable energy curtailment. The study simulates twenty-eight scenarios and sensitivities using a unit commitment and economic dispatch model of a 2030 European power system that acknowledges that coal will not be fully eliminated from the 2030 generation mix. The results show that interconnection and battery deployment can alleviate renewable energy curtailment by over 2.4 TWh for the basecase, but a high carbon price is critical to ensuring their deployment plays a complementary role in reducing emissions. The paper also examines the impact of batteries and interconnection on solar and wind energy curtailment. It reveals that battery development nearly doubled the solar energy benefits achieved by interconnection development in some cases, while wind energy benefited more from interconnection development.

Risk-Averse Stochastic Midterm Scheduling of Thermal-Hydro-Wind System: A Network-Constrained Clustered Unit Commitment Approach

With the increasing penetration of renewable energy, designing the efficient midterm (i.e., annual) schedule for multi-source power systems is valuable in providing substantial cost reductions and flexibilities while ensuring system security against uncertainties of renewables. This paper presents a risk-averse two-stage stochastic midterm scheduling model for thermal-hydro-wind systems against short-term uncertainties of renewable generation. A network-constrained clustered unit commitment (NC-CUC) model is proposed to effectively incorporate short-term operation constraints into the midterm scheduling model, for improving the solution accuracy while reducing computational burden. Specifically, as the traditional CUC model cannot ensure consistent unit commitment status of generators across all scenarios, a power flow-based unit clustering method is proposed to improve the accuracy of short-term operation results. In addition, the conditional value-at-risk (CVaR) is used to quantify the system operational risk caused by uncertainties of wind generation and natural water inflows. Case studies show that the risk-averse two-stage stochastic midterm scheduling model can ensure operation dynamics and solution efficiency, and the proposed clustering method can achieve a tradeoff between solution accuracy and computational burden for the stochastic network-constrained CUC model.

Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty

With the increasing penetration of volatile renewable energy sources (RES) in power systems, the formulation of RES uncertainty set in the unit commitment (UC) problem poses significant challenges to the operators especially those in AC-DC systems. To overcome the over-conservativeness and computational redundancy in conventional uncertainty formulation methods, this paper induces the stochastic rationale into the robust dispatch model and proposes a two-stage robust frequency risk-constrained unit commitment (FRCUC) model to excavate the system flexibility. Based on the typical characteristics of AC-DC dispatch system, the frequency violation risk (FVR) factors are proposed to enhance the adaptability of uncertainty sets, wherein the HVDC regulation is activated to cope with the extreme uncertain scenarios with lower probability. As a result, being aware of the wind power distribution, the proposed model succeeds in managing the operational risks by fully exploiting the controllability of HVDC system, and the over-conservativeness from conventional robust model is handled by the proposed FVR factors. The outperformance of our model is validated by the numerical experiments on a modified AC-DC system, it is validated that the operational risks are evaluated when considering the violated wind power distribution, and therefore the model economic benefits are promoted when ensuring the reliability.

Publisher’s Note

High-voltage direct current (HVDC) transmission systems are widely employed to transmit energy among systems with renewable energy integrated. However, the reduced system inertia and the risk of HVDC failure pose significant challenges to operators. To ensure system reliability especially frequency stability under extreme circumstances, a robust frequency-constrained unit commitment (FCUC) model is proposed in this paper. Unlike previous studies, an extra power-rescheduling procedure is embedded in our model, and consequently the FCUC model is formulated as a three-layer structure, where the day-ahead schedule model, emergency corrective model (primary frequency response), and power rescheduling (secondary frequency response) are co-optimized. Accordingly, an improved tri-level Benders Decomposition (BD) algorithm was developed to solve the problem. By leveraging the optimality cuts of the algorithm, the characteristics of fast power support and flexible regulation capabilities of HVDC system in real-time (RT) market are preemptively considered in DA market. Consequently, the model feasible region is more adaptive to the plausible contingencies The outperformance of our model was validated by the numerical experiments in an asynchronous HVDC interconnected system. The results demonstrate that by formulating the coordination of AC–DC dispatch framework, the global optimal operational cost is further captured when ensuring the system reliability.

Modeling the operational flexibility of natural gas combined cycle power plants coupled with flexible carbon capture and storage via solvent storage and flexible regeneration

In electricity systems with high shares of variable renewable energy resources, the capability to flexible alter power output can increase the economic value of natural gas combined cycle (NGCC) power plants equipped with carbon capture and sequestration (CCS) and enhance their competitiveness as firm low-carbon resources. Here we examine NGCC power plants w/CCS (NGCC-CCS) coupled with solvent storage to enable flexible operation. We present a modular and detailed modeling formulation to represent these systems in a computationally efficient and accurate manner and to evaluate the operating patterns and system value of flexible CCS designs. The proposed framework breaks down NGCC-CCS plants into major subcomponents and uses linear constraint formulations to enforce energy and mass balances. In addition, thermal power plants are subjected to unit commitment (UC) constraints that are generally time-consuming to solve via conventional methods, which use binary decision variables for start-up and shut-down decisions. We thereby investigate whether the linear relaxation of discrete UC decision variables coupled with a generator clustering method are applicable to model flexible CCS subcomponents. Finally, we integrate this novel flexible CCS formulation into a power system unit commitment and economic dispatch model to present a case study that shows the hourly operating patterns of NGCC-CCS subcomponents and impacts on power system environmental and economic performance. As compared to a conventional binary UC formulation, our results show that linear relaxation of a generator clustering formulation has much faster runtime (18–527 times faster), with errors of 0.01%-3.4% across all the evaluated metrics. The operating patterns and system performance suggest our modeling framework can accurately and tractably capture both plant-level and system-level outcomes. Finally, our results demonstrate that flexible NGCC-CCS systems decrease power system operating costs by increasing power output during peak demand periods to displace higher marginal cost units. We also demonstrate that the system-level benefits provided by flexible operation of NGCC-CCS plants will be increasingly valuable to the future grids with high penetration of variable renewable resources.

A Solution Method for Many-Objective Security-Constrained Unit Commitment Considering Flexibility

With the increasing penetration of wind power into power systems and the random fluctuation of the wind farm (WF) output, system flexibility must be considered in the optimal generation dispatch. Based on the extreme scenarios of the WF output, we proposed a flexibility risk index to evaluate the system flexibility of each time interval. We established a five-objective security-constrained unit commitment (SCUC) model of a power system with WFs, thermal generation plants, battery energy storage stations, and pumped storage hydro stations. The objectives were to minimize the system flexibility risk, total network loss, operation cost, power purchase cost, and pollutant gas emissions. To obtain the Pareto optimal solutions of the model, based on the objective selection and ε-constraint methods, we proposed two methods to reduce the dimension of objectives and transformed the five-objective optimization model into a series of three-objective optimization models. Then, we used the normalized normal constraint method to solve the Pareto frontier surface of each three-objective optimization model. We used a color column to represent the value change of the two upper layer objectives and visualized the Pareto frontier of the five-objective SCUC model in the three-dimensional ordinate space. Case studies on the modified IEEE-9 bus system and an actual power grid demonstrated the effectiveness and high computational efficiency of the proposed method.

A techno-economic assessment of CO2 capture in biomass and waste-fired combined heat and power plants – A Swedish case study

The need to reduce global CO2 emissions is urgent and might be facilitated by carbon capture and storage (CCS) technologies. Sweden has a goal to reach net-zero emissions by 2045. Negative emissions and bio-CCS (BECCS) have been proposed as important strategies to reach this target at the lowest cost. The Swedish district heating sector constitutes a large potential for BECCS, with biogenic point sources of CO2 in the form of combined heat and power (CHP) plants that burn biomass residues from the forest industry. This study analyzes the potential of CO2 capture in 110 existing Swedish biomass or waste-fired CHP plants. Process models of CHP steam cycles give the impacts of absorption-based CCS on heat and electricity production, while a district heating system unit commitment model gives the impact on plant operation and the potential for CO2 capture. The results provide a cost for carbon capture and transport to the nearest harbor by truck: up to 19.3 MtCO2/year could be captured at a cost in the range of 45–125 €/tCO2, corresponding to around 40% of the total fossil fuel-based Swedish CO2 emissions. This would be sufficient to meet a proposed target of 3–10 Mt/year of BECCS by 2045.

Day-Ahead Operational Scheduling With Enhanced Flexible Ramping Product: Design and Analysis

New resource mix, e.g., renewable resources, are imposing operational complexities to modern power systems by intensifying uncertainty and variability in the system net load. This issue has motivated independent system operators (ISOs), e.g., California ISO (CAISO), to add flexible ramping products (FRPs) to their day-ahead (DA) market models. Such structural changes in the DA market formulation require further analyses and detailed design to ensure adequate operational flexibility, market efficiency, and reliability. This paper conducts a comprehensive study to: (a) augment existing DA market models with enhanced FRP design to schedule ramp capabilities that are more adaptive with respect to the real-time (RT) 15-min net load variability and uncertainty, and (b) design corresponding market payment policies that accurately reflect the value of the added flexibility through enhanced FRP design. The proposed FRP design can be implemented in present-day system operations with minimal disruption to existing DA market models. Performance of the proposed DA market model, which includes the enhanced FRP design, is compared against the DA market model with existing FRP design through a validation methodology based on RT unit commitment model. This validation methodology mimics fifteen-minute markets of CAISO. The proposed method is tested on an IEEE 118-bus test system.