Unit Commitment Model Research Articles

Role of optimal transmission switching in accommodating renewable energy in deep peak regulation-enabled power systems

Due to the shortage of fossil energy and the pollution caused by combustion of fossil fuels, the proportion of renewable energy in power systems is gradually increasing across the world. Accordingly, the capacity of power systems to accommodate renewable energy must be improved. However, integration of a large amount of renewable energy into power grids may result in network congestion. Hence, in this study, optimal transmission switching (OTS) is considered as an important method of accommodating renewable energy. It is incorporated into the operation of a power grid along with deep peak regulation of thermal power units, forming an interactive mode of coordinated operation of source and network. A stochastic unit commitment model considering deep peak regulation and OTS is established, and the role of OTS in promoting the accommodation of renewable energy is analyzed quantitatively. The results of case studies involving the IEEE 30-bus system demonstrate that OTS can enable utilization of the potential of deep peak regulation and facilitate the accommodation of renewable energy.

Systematic Categorization of Optimization Strategies for Virtual Power Plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

Modeling of Third Party Access Tariffs and Portfolio Gas Purchases of CCGTs in the Self-Unit Commitment Problem

The structure of the operational costs of gas fired units (GFU) in real power systems is much more complex than what standard unit-commitment (UC) models consider in the state-of-the-art formulations. On the one hand, when a generation company owning several GFUs procures its gas -either totally or partially- at the gas spot market, the final fuel price will depend on the total gas purchases. This fact couples the operation of all the GFUs as it is impossible to build individual cost functions for each generator. On the other hand, as any other gas consumer, the generation company has to pay the gas Third Party Access (TPA) tariffs. The generation company must contract in advance the monthly TPA capacity for each gas exit point and depending on such decision, the final variable cost of the GFUs will be different. This paper states the importance of modeling properly both issues and presents a novel mathematical formulation that can be embedded in a self-UC model. The presented example case highlights the benefits of the proposed formulation.

Robust risk‐averse unit commitment with solar PV systems

A challenge for energy market operators (EMOs) is to render a cost-effective generation schedule, known as unit commitment (UC), for the power system with intermittent renewable power generations. Consolidating the risk management concept and robustness into the UC not only helps EMO to maintain the secure operation of power systems but also informs the EMO about the tail-risk cost. Based on this motivation, the authors have proposed a model for UC with worst-case conditional value-at-risk (UC-WCVaR) with intermittent solar generations and loads. Unlike intractable risk-averse stochastic UC models, this model minimises the worst-case CVaR of the dispatch cost over an ambiguity set of the probability distributions. This robust tractable model relies on the historical data that can partially characterise the underlying probability distributions of solar photovoltaic (PV) systems and loads. They solved this model by its equivalent tractable robust counterpart using linear decision rules. The tests conducted on the IEEE 118-bus test system demonstrate that: (i) UC-WCVaR solution matches the results derived from normally distributed solar samples; (ii) the UC-WCVaR is less conservative than the classic robust unit commitment. Also, the computation time is directly proportional to the number of uncertain resources and inversely proportional to the number of stages.

Flexible operation of low-inertia power systems connected via high voltage direct current interconnectors

The replacement of conventional synchronous generators with converter-interfaced generation units calls for increased amounts of flexibility. This paper proposes a novel formulation of the security-constrained unit commitment (SCUC) model applied to a multi-area power system connected via High Voltage Direct Current (HVDC) links. From a system perspective, this paper provides a critical analysis of the synergies and differences between the exploitation of thermostatic loads and HVDC links when providing different layers of flexibility to the system. The former units operate within a local dimension, while the latter enable cross-border exchange of flexibility. Eight different ancillary services are modelled to tackle generation/load outages and uncertainty/variability in renewable energy output. The model is applied to the Great Britain network, which is connected to the Irish network and to the one in Continental Europe. Results suggest a critical review of the operation of future low-carbon HVDC-interconnected systems. Feasibility studies on the benefit for interconnection should no longer neglect considerations on local post-fault frequency dynamics in each area of the system. Then, fundamental changes to the mechanisms that price ancillary services become necessary in order to align these mechanisms with the technical needs of the system.

Tax Reform for the Energy Transition in Korea’s Power Generation Sector

The tax structure capable of achieving an energy transition in the power sector was analyzed by applying the Pigouvian tax on generation fuels. Under the 2018 Tax Act Amendment, the tax rate criteria for the excise tax on power generation fuels changed from the calorific value to environmental externalities of the fuel. However, to reverse the merit order of bituminous coal generation with liquefied natural gas (LNG) generation, reflecting only some external costs of the environment as a tax is not enough. In this paper, we established four tax reform scenarios for bituminous coal and LNG considering environmental externalities, and we analyzed the reversal of dispatch priority using the electricity system unit commitment and M-Core economic dispatch model. According to the analysis results, the share of bituminous coal generation will be reduced to 10–20% depending on the scenario, reflecting the relative tax rate equalizing the fuel costs of bituminous coal and LNG power. To achieve an energy transition by reversing the merit order of bituminous coal and LNG generation, the tax rate of bituminous coal must be more than twice that of LNG. Moreover, to achieve an eco-friendly generation mix through tax reform, the external costs of the environment by fuel source should be accurately estimated and efficient taxation that can adequately reflect these external costs of the environment while considering tax fairness, neutrality and simplicity should be established.

Multi-level interactive unit commitment of regional power system

Distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG + ADN + MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems. To explore the synergistic ability of the TG + ADN + MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainty of wind power is well modeled by data driven, which is proposed in our previous work (Zhang et al., 2019) [1]. Both the startup/shutdown variables of the thermal units and the variables in TG + ADN + MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system. An improved 6-bus system is used to test the proposed model, the numerical results show that the proposed decentralized algorithm is a fully parallelized procedure. And it also demonstrates the parallel implementation significantly enhances computations efficiency of the ATC algorithm.

Evaluating the effect of electric vehicle parking lots in transmission-constrained AC unit commitment under a hybrid IGDT-stochastic approach

Power network operators have recently faced new challenges due to an increase in the penetration of non-dispatchable renewable energy sources in power grids. Incorporating emerging flexible resources like electric vehicle parking lots (EVPLs) and demand response programs (DRPs) into power systems, could be a good solution to deal with inherent uncertainties imposed by these resources to the power grid. EVPLs can improve power system operating conditions by active and reactive power injection capabilities. The participation of consumers in DRPs can also improve energy consumption management by decreasing or shifting loads to other periods. This paper proposes a hybrid information gap decision theory (IGDT)- stochastic method to solve a transmission-constrained AC unit commitment model integrated with electric vehicle (EV), incentive-based DRP, and wind energy. The behavioural uncertainty related to EV owners is modelled using a scenario-based method. Additionally, an IGDT method is applied to manage wind energy uncertainty under a two-level optimization model. Verification of the proposed model is done under several case studies. Based on the results achieved, the proposed risk-based hybrid model allows the operator to differentiate between the risk level of existing uncertainties and apply a high-flexibility decision-making model to deal with such difficulties. Additionally, the role of the aforementioned flexible resources in the reduction of power system running costs and wind power uncertainty handling are evaluated. Numerical results confirm a 3.7% reduction in the daily operating costs as a consequence of coordinated scheduling of EVPL and DRP. Moreover, Taking advantage of reactive power injection of EVPL provides more cost savings.

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles (EVs), an aggregator-based demand response (DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator (ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

Addressing the Conditional and Correlated Wind Power Forecast Errors in Unit Commitment by Distributionally Robust Optimization

In this paper, a study of the day-ahead unit commitment problem with stochastic wind power generation is presented, which considers conditional, and correlated wind power forecast errors through a distributionally robust optimization approach. Firstly, to capture the characteristics of random wind power forecast errors, the least absolute shrinkage, and selection operator (Lasso) is utilized to develop a robust conditional error estimator, while an unbiased estimator is used to obtain the covariance matrix. The conditional error, and the covariance matrix are then used to construct an enhanced ambiguity set. Secondly, we develop an equivalent mixed integer semidefinite programming (MISDP) formulation of the two-stage distributionally robust unit commitment model with a polyhedral support of random variables. Further, to efficiently solve this problem, a novel cutting plane algorithm that makes use of the extremal distributions identified from the second-stage semidefinite programming (SDP) problems is introduced. Finally, numerical case studies show the advantage of the proposed model in capturing the spatiotemporal correlation in wind power generation, as well as the economic efficiency, and robustness of dispatch decisions.

Considerations for maintaining resource adequacy of electricity systems with high penetrations of PV and storage

Solar photovoltaic (PV) and energy storage deployment has grown rapidly in recent years and is expected to continue as the costs of these technologies continues to decline. As PV penetration grows, peak net load hours—where net load is load minus variable renewable energy generation—become increasingly important for understanding resource adequacy of power systems. In this work, we evaluate scenarios of a future U.S. power system with high penetrations of PV. We do so by using a national-scale capacity expansion model to consider 15 scenarios with national annual PV generation penetration levels of 16%–47%, combined wind and PV penetrations are 40%–64%, and storage capacity ranging from 5% to 47% of peak demand. The modeled scenarios incorporate 7-years of coincident weather data and load profiles (2007–2013) for the contiguous United States. In doing so, we evaluate when peak net load hours occur and how their occurrence is impacted by PV penetration, region, and weather. We find that with increasing PV penetration, peak net load hours move from afternoon periods to evenings or from midmorning to early morning, and that the frequency of winter peak net load hours increases. We also simulate hourly system dispatch in 3 of the 15 scenarios using a commercially available unit commitment and dispatch model—PLEXOS-- to evaluate with much greater resolution how the systems behave under specific weather conditions observed from 2007 to 2013. The hourly results show substantial shifts in how peak net load hours are served as PV penetration increases, and that how the system serves those top peak net load hours varies considerably across different regions of the country. Finally, we employ a simple method for estimating the impacts of snow cover in the high PV penetration scenarios and find that for the moderate and highest levels of PV we examine, snow cover has the potential to result in capacity and energy shortfalls.

Systemic Evaluation of the Effects of Regional Self-Supply Targets on the German Electricity System Using Consistent Scenarios and System Optimization

This paper analyses the effects of regional renewable electricity self-sufficiency targets on the power system in Germany. For this purpose, an interdisciplinary approach from social sciences and energy system modelling was chosen, which allows considering qualitative factors such as public acceptance or political stability. Following the concept of context scenarios, consistent raw scenarios are generated by a cross-impact balance analysis (CIB), and the scenarios are quantified by the unit commitment and expansion cost minimisation model ENTIGRIS considering power plants, storages, and the electricity grid. This approach enables an understanding of the system framework conditions and their relationships and allows the combination of qualitative and quantitative scenario descriptors. The most important factors for setting regional self-sufficiency targets were identified through interviews. The main system effects identified are: The regional distribution of generation capacities is strongly influenced by a more demand-oriented installation of generation capacities. This leads to less grid reinforcement, but higher rates of curtailment. In all scenarios, higher utilization of the PV roof potential instead of ground mounted could be observed. The total system costs are increasing only slightly with regional self-supply targets. In general, it was found that the influence of regional self-sufficiency targets is less pronounced in scenarios that already achieve high national RES shares than in scenarios that achieve lower shares, since technology, storage and grid expansion measures are necessary anyway to achieve high RES shares. Overall, the effects here are rather small and the regional objective is not associated with major disadvantages for the system. In a future characterised by stagnation, the system can benefit from regional targeting, as higher renewable shares and lower costs can result. The main conclusion therefore is that regional target setting seem to be beneficial for the overall power system, in terms of system cost, national RE share, acceptance and CO2-emissions.

Data-driven robust day-ahead unit commitment model for hydro/thermal/wind/photovoltaic/nuclear power systems

According to the complementary characteristics of various power sources, this paper establishes a data-driven robust day-ahead unit commitment model for a hydro-thermal-wind-photovoltaic-nuclear power system that can be used by the independent system operaters (ISOs). A data-driven robust optimization method based on the robust kernel density estimation (RKDE) is employed to deal with the uncertainties of wind and photovoltaic (PV) power. That is, the distributional information of wind and PV power is extracted by RKDE from the big data, then it is incorporated into the data-driven uncertainty set, and finally a robust optimization model is formed. In view of the facts that the conventional water spillage methods fail to pay equal attention to the benefits of the basin and the individual hydro plants, eight kinds of strategies that can make the water spillages or hydropower curtailments distributed proportionally in each hydro plant are proposed. In addition, the operating model of nuclear power unit involved in peak load regulation is established to promote its operational flexibility. The numerical results and simulation on a modified New England 39-bus system verify the superiority and practicability of the proposed model.

Exploiting flexibility of combined-cycle gas turbines in power system unit commitment with natural gas transmission constraints and reserve scheduling

With the integration of large-scale renewable energy including wind power into the power grid, efficient flexible resources are required for wind power accommodation. The combined-cycle gas turbines (CCGTs) are widely used in the power system with flexible operation characteristics, which leads to enhanced interdependencies between the power grid and the natural gas network. In this paper, a unit commitment (UC) model with natural gas transmission constraints and reserve scheduling considering the flexibility of CCGT with quick startup-shutdown and flexible operation characteristics is proposed, to reduce wind power curtailment and high startup and shutdown cost of conventional generation units in the power system. Considering the complex operating conditions of CCGT and the non-convexity of Weymouth equation in natural gas network, this paper adopts an improved mode-based model (MBM) for modeling of CCGT and converts the Weymouth equation into a second-order cone relaxation (SOCR) formulation with penalty term through a concave-convex process (CCP) to make the relaxation tight, thus transforming the UC model into an iterative mixed-integer second-order cone programming (MISOCP) problem to be efficiently solved. The effectiveness of the proposed model on reducing wind power curtailment and startup-shutdown cost is demonstrated through case studies.

Energy transition in the South East Europe: The case of the Romanian power system

With the exception of Albania, the countries of South-Eastern Europe (SEE) have high shares of electricity generation from an ageing fleet of coal-fired power units with quite low efficiencies. Energy planning-related decision-making regarding whether to modernize or replace a significant share of this old generating capacity has to be fixed within a global clean energy transition context. At the same time, the SEE region has a huge potential for renewable energy deployment and energy efficiency implementation. This work presents a generic mixed integer linear programming model to determine the optimal energy scheduling of a multi-zonal power system following the roadmaps of its energy transition process. Through a detailed unit commitment model implementing a co-optimization of energy and reserves market with a cost minimization objective function, the developed approach determines the optimal annual energy mixes of the Romanian power system through the day by day iterative solution of all the dates of three specific time milestones (2020, 2030, and 2040), where the outputs of a specific date comprise the inputs of the next one. The model outputs highlight the impacts on the power system in terms of technical, economic, and environmental performance. The energy transition towards a renewables dominant capacity and production mix guarantees the system’s security of supply, and improves noticeably its environmental performance, reporting a significant decrease in the amount of resulting CO2 emissions. With regard to the economic impacts, the wholesale price follows an increasing trend due to the parallel increase of the CO2 emission pricing, on the grounds that natural gas-fired units and electricity interconnections set the system’s price during most of the hours of all dates and years. The developed optimization framework enables the provision of useful insights on the determination of the optimal energy roadmaps, by highlighting the influences and the challenges of each phase.

Stochastic Unit Commitment in Low-Inertia Grids

In this paper, the Unit Commitment (UC) problem in a power network with low levels of rotational inertia is studied. Frequency-related constraints, namely the limitation on Rate-of-Change-of-Frequency (RoCoF), frequency nadir and steady-state frequency error, are derived from a uniform system frequency response model that incorporates dynamics and controls of both synchronous generators and grid-forming inverters. These constraints are then included into a stochastic UC formulation that accounts for wind power and equipment contingency uncertainties using a scenario-tree approach. In contrast to the linear RoCoF and steady-state frequency error constraints, the nadir constraint is highly nonlinear. To preserve the mixed-integer linear formulation of the stochastic UC model, we propose a computationally efficient approach that allows to recast the nadir constraint by introducing appropriate bounds on relevant decision variables of the UC model. This method is shown to be generally more accurate and computationally more efficient for medium-sized networks than a piece-wise linearization method adapted from the literature. Simulation results for a modified IEEE RTS-96 system revealed that the inclusion of inertia-related constraints significantly influences the UC decisions and increases total costs, as more synchronous machines are forced to be online to provide inertial response.

A novel robust security constrained unit commitment model considering HVDC regulation

To further improve the robustness and economic efficiency of the conventional security-constrained unit commitment (SCUC) problem when confronting system uncertainties, we propose a novel robust SCUC model considering the prompt and flexible controllability of high voltage direct current (HVDC) in asynchronous interconnected systems. Unlike the conventional method, the proposed model minimizes the total cost of pre-day commitment for the base case and real-time dispatch under extreme uncertainties when guaranteeing the generation units and HVDC can be adjusted adaptively and securely. The robustness and economic efficiency of the model are enhanced by optimizing their trade-off when integrating HVDC regulation. As the method involves unit commitment, short-term dispatch, and power rescheduling, an improved tri-level Benders decomposition algorithm is developed according to the structure of the model to solve this mixed-integer nonlinear programming problem. In computational experiments, the effectiveness of the proposed model was validated in a two-area HVDC interconnected asynchronous system under various uncertainties. Additionally, the superior cooperation between a day-ahead commitment plan and real-time dispatch in the novel SCUC model with HVDC regulation was verified.

Data-Driven Adjustable Robust Unit Commitment of Integrated Electric-Heat Systems

This paper proposes a data-driven adjustable robust unit commitment (UC) model for integrated electric-heat systems (IEHSs), where the data-driven, and robustness adjustable features are endowed by the novel uncertainty modeling method. The devised uncertainty set is the intersections of a series of hyperplanes. It combines the merits of the traditional box-like uncertainty set, whose underlying computation burden is moderate, and the convex hull formed by historical data, which would lead to less conservative operation strategies. The boundaries of the bounding high-dimensional rectangle of the uncertainty set are set as decision variables to achieve the balance between the operation costs, and risk. The two-stage decision-making framework is employed in the robust UC model of IEHSs, in which the district heating networks are operated at the variable-flow-variable-temperature mode. The modified column-and-constraint generation (C, and CG) algorithm is employed to solve the proposed robust program, where an additional feasible region reduction step is embedded to decrease the computation burden of the worst case identification. Simulation results on two test systems verify the effectiveness of the proposed methods.

Managing Multitype Capacity Resources for Frequency Regulation in Unit Commitment Integrated With Large Wind Ramping

An efficient unit commitment planning must consider frequency regulation capacity in the model. Such models are more complicated under a high penetration level of renewable energy because of renewable ramping and uncertainty. This paper addresses these issues in the unit commitment. The proposed model for unit commitment considers uncertainty and ramping of wind power, frequency regulation capacity, spinning reserve, demand response, and pumped-storage hydroelectricity. Two reserve capacities including primary frequency regulation and spinning reserve are designed to handle the intermittency and ramping of renewable energies. In order to optimize the costs, the pumped-storage hydroelectricity and demand response program are also included to deal with ramping and uncertainty. The numerical results specify that the arrangement of frequency regulation capacity, pumped-storage system and demand response can effectively tackle both the ramping and uncertainty. The system includes 10-generator with total power equal to 1070 MW and one wind generator with 300 MW power. The initial wind integration level is about 28%. It is verified that decreasing the frequency regulation capacity by 10% reduces wind integration level by 94%. The demand response and pumped-storage increase wind integration level by 10% and 16%; while both together increase wind integration by 25% compared to the initial level. The wind integration level without large wind ramping can be increased up to 200%.

Energy and reserve markets: interdependency in electricity systems with a high share of renewables

Markets for electric energy and reserves are linked as the same physical assets operate in both markets. Two different designs are commonly used for clearing energy and reserve markets; joint clearing or sequential clearing. It’s generally accepted that joint clearing results in higher (or at least the same) social welfare than a pure sequential clearing. However, the impact of an increasing share of intermittent renewables on the interdependency between energy and reserves has been underexposed in literature. This paper studies the coupling between day-ahead energy markets and reserve markets for allocation and activation, and this for different shares of intermittent renewables (i.e. wind and solar PV). Towards this end, a large scale unit commitment model is deployed to a case study inspired by the Central Western European system. Different scenarios, developed within the EU-SysFlex project, are considered. The study indicates that the interdependency between energy and reserve markets increases with increasing share of intermittent renewables. As such, the cost difference, evaluated in real-time, between joint and sequential clearing increase as well with increasing share of intermittent renewables. The analysis shows that this impact can be partially compensated by an increasing role of renewables and flexible load in reserve provision.