Day-ahead Unit Commitment Model Research Articles

Adaptive time period aggregation for Day-ahead unit commitment

The growing penetration of renewables calls for finer temporal resolutions of the day-ahead unit commitment (DA-UC) problem in order to accommodate the fluctuations and improve the performance of UC schedules, leading to the expansion of the DA-UC problem scale. However, due to its high computational burden, the viability of adopting larger UC models in practice becomes a challenge. In this paper, an adaptive time period aggregation strategy and a corresponding DA-UC model are proposed to reduce the model scale while preserving feasibility and accuracy. The aggregation strategy integrates the statistical characteristics of net demand with the operational characteristics of thermal units to effectively capture the consistency in on/off statuses. Case study based on the IEEE 118-bus system validates that, combined with a post-processing procedure, the proposed strategy and model can provide near-optimal UC schedules with approximately 5 times acceleration for various demand profiles, enhancing computational efficiency.

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.

A Bi-Level Optimization Formulation of Priority Service Pricing

Priority service pricing is a promising approach for mobilizing residential demand response, by offering electricity as a service with various levels of reliability. Higher levels of reliability correspond to higher prices. Proper pricing guarantees that consumers self-select a level of reliability that corresponds to the reliability that the system can offer. However, the traditional theory for menu design is based on numerous stringent assumptions, which may not be respected in practice, such as well-behaved convex cost functions for generators. In this article, we design a priority service menu as the equilibrium solution to a Stackelberg game, which is modeled as a bi-level optimization problem involving a vertically integrated utility and consumers. We reformulate the equilibrium as a mixed-integer problem. As a consequence of this approach, we can integrate the menu design problem within a day-ahead unit commitment model. This allows us to design a menu which exactly meets the profit requirements of a firm. The approach is illustrated on a toy numerical example as well as a large-scale model of the Belgian power market.

Stochastic reserve scheduling of energy storage system in energy and reserve markets

Energy storage systems (ESSs) can be used to participate in both the energy and reserve markets to maximize their reserve benefits. In contrast to traditional thermal units, ESSs have three statuses: charging status, discharging status, and idling status. The energy-limited feature of ESSs makes it difficult to schedule the reserve in the joint energy and reserve markets. In this paper, a detailed energy and reserve model of ESSs is considered in the joint stochastic day-ahead unit commitment model. The main highlights of this paper are the reserve model of ESSs. Compared to existing studies, a proposed six-mode hourly power reserve model of ESSs can schedule more reserve amount to participate in the reserve market. The six reserve modes include more charging/discharging, less charging/discharging, and switch from another status to charging/discharging. Besides, multi-hour coupled reserve constraints are introduced to avoid ESSs failures to provide the reserves scheduled in the day-ahead market. Considering the uncertainty of demand and renewable generation, a scenario-based stochastic model is adopted to calculate both the energy and reserve costs of the whole system. Two different systems are used to discuss the benefits of the proposed reserve model and analysis on some crucial parameters.

Integrating flexible demand response toward available transfer capability enhancement

Available transfer capability (ATC) has been widely adopted as a crucial measure to guarantee free and reliable power trading. The existing literature generally evaluates the ATC of a power grid considering inelastic load. However, the impacts of responsive load have not yet been systematically explored, which underestimates the ability of demand side resources in ATC enhancement. Therefore, we propose a two-stage ATC evaluation framework in this paper integrating flexible demand response (FDR). In the first stage, i.e., a day-ahead market, a demand side bidding scheme is developed in which electricity users can make flexible choices to participate in demand response (DR) programs according to their preferences. In this paper, FDR is categorized into three typical patterns: (i) deferrable DR, (ii) switchable DR and (iii) adjustable DR. The objective is to minimize the total generation and dispatch costs, while respecting the constraints for FDR, the minimal accommodation of renewable energy, the inter-area power trading, etc. Then based on generation and demand side bidding, a day-ahead unit commitment model is formulated to schedule thermal generators, interchange power and FDR. In the second stage, a real-time ATC evaluation model based on the aforementioned unit commitment results is proposed to quantify the contributions of FDR on ATC enhancement. Case studies based on IEEE 14-bus system and a provincial power grid in China demonstrate that FDR can effectively improve real-time ATC by load shifting and peak shaving, and facilitate the accommodation of renewable energy.

Flexibility Based Day-Ahead Generation–Reserve Bilevel Decision Model

With the increasing penetration rate of renewable energy like fluctuating wind and solar energy, the power system has to equip itself with a more reasonable reserve capacity. Therefore, how to quantify the reserve capacity needed for dealing with the uncertainty and fluctuation has turned out to be a new problem faced by the power system integrated with large-scale renewable energy. This paper proposes a flexibility based day-ahead generation–reserve bilevel decision model. In the upper level, the day-ahead unit commitment model is constrained by flexibility reserve, which is calculated in the lower level. In the lower level, taking into account various factors of uncertainty and fluctuation, e.g., wind power ramping, load ramping and random failure of conventional units, the ramping probability distribution of an equivalent system is obtained by the universal generating function method, then the quantified relationship between operating reserve and flexibility is established ultimately. If the unit commitment scheme gained from the upper level could not provide sufficient reserve, a feedback for the correction of the upper level is needed. The rationality and validity of the proposed model are verified through the simulation of a modified IEEE-118 bus system.

Power System Day-Ahead Unit Commitment Based on Chance-Constrained Dependent Chance Goal Programming

In the context of large-scale renewable energy integrated into an electrical power system, the effects of power forecast errors on the power balance equation of the power system unit commitment model is considered. In this paper, the problem of solving the power balance equation with uncertain variables was studied. The unit commitment model with random variables in the power balance equation was solved by establishing a power system day-ahead optimisation unit commitment model based on chance-constrained dependent chance goal programming. First, to achieve the solution of the power balance equation with random variables, the equality constraint is loosened into an inequality constraint, and the power balance equation constraint is transformed into a dependent chance programming model aimed at maximising the probability of occurrence of random events in an uncertain environment. Then, the dependent chance programming model is proposed to ensure the economy and security of the scheme, and the goal programming model is introduced to facilitate an efficient solution. By combining dependent chance programming and goal programming, a power system day-ahead unit commitment model based on chance-constrained dependent chance goal programming is established. Finally, an example is discussed to demonstrate the effectiveness of the proposed model.

Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

With the increasing penetration of wind power and demand response integrated into the grid, the combined uncertainties from wind power and demand response have been a challenging concern for system operators. It is necessary to develop an approach to accommodate the combined uncertainties in the source side and load side. In this paper, the fuzzy stochastic conditional value-at-risk criterions are proposed as the risk measure of the combination of both wind power uncertainty and demand response uncertainty. To improve the computational tractability without sacrificing the accuracy, the fuzzy stochastic chance-constrained goal programming is proposed to transfer the fuzzy stochastic conditional value-at-risk to a deterministic equivalent. The operational risk of forecast error under fuzzy stochastic conditional value-at-risk assessment is represented by the shortage of reserve resource, which can be further divided into the load-shedding risk and the wind curtailment risk. To identify different priority levels for the different objective functions, the three-stage day-ahead unit commitment model is proposed through preemptive goal programming, in which the reliability requirement has the priority over the economic operation. Finally, a case simulation is performed on the IEEE 39-bus system to verify the effectiveness and efficiency of the proposed model.

Hidden power system inflexibilities imposed by traditional unit commitment formulations

Approximations made in traditional day-ahead unit commitment model formulations can result in suboptimal or even infeasible schedules for slow-start units and inaccurate predictions of actual costs and wind curtailment. With increasing wind penetration, these errors will become economically more significant. Here, we consider inaccuracies from three approximations: the use of hourly intervals in which energy production from each generator is modeled as being constant; the disregarding of startup and shutdown energy trajectories; and optimization based on expected wind profiles. The results of unit commitment formulations with those assumptions are compared to models that: (1) use a piecewise-linear power profiles of generation, load and wind, instead of the traditional stepwise energy profiles; (2) consider startup/shutdown trajectories; and (3) include many possible wind trajectories in a stochastic framework. The day-ahead hourly schedules of slow-start generators are then evaluated against actual wind and load profiles using a model real-time dispatch and quick-start unit commitment with a 5min time step. We find that each simplification usually causes expected generation costs to increase by several percentage points, and results in significant understatement of expected wind curtailment and, in some cases, load interruptions. The inclusion of startup and shutdown trajectories often yielded the largest improvements in schedule performance.

Unit Commitment With Continuous-Time Generation and Ramping Trajectory Models

There is increasing evidence of shortage of ramping resources in the real-time operation of power systems. To explain and remedy this problem systematically, in this paper we take a novel look at the way the day-ahead unit commitment (UC) problem represents the information about load, generation and ramping constraints. We specifically investigate the approximation error made in mapping of the original problem, that would decide the continuous-time generation and ramping trajectories of the committed generating units, onto the discrete-time problem that is solved in practice. We first show that current practice amounts to approximating the trajectories with linear splines. We then offer a different representation through cubic splines that provides physically feasible schedules and increases the accuracy of the continuous-time generation and ramping trajectories by capturing sub-hourly variations and ramping of load in the day-ahead power system operation. The corresponding day-ahead UC model is formulated as an instance of mixed-integer linear programming (MILP), with the same number of binary variables as the traditional UC formulation. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total day-ahead and real-time operation cost, and the number of events of ramping scarcity in the real-time operations.

Corrective Transmission Switching for N-1-1 Contingency Analysis

System operators are required to serve the load in the most cost-effective way while maintaining the integrity of the system and heeding reliability requirements. In the day-ahead market, operators acquire reserves in an attempt to guarantee N -1 reliability; yet, reserve deliverability is not guaranteed. Prior research has shown that the use of transmission switching, or topology control, may help improve reserve deliverability. In this paper, transmission switching is used as a corrective mechanism to help the system achieve N -1-1 reliability, where not only has the system lost a single element, but also it experiences the loss of a second major element after an adjustment period. In an attempt to preserve N -1-1 reliability, for this paper, a day-ahead unit commitment model that acquires supplementary reserves is solved. The day-ahead market solution is then tested for N -1-1 reliability using contingency analysis models with and without transmission switching. The methodology can be employed at the day-ahead time stage to ensure the system has acquired sufficient supplemental reserves. The results demonstrate that not only can corrective transmission switching be beneficial post-contingency without inhibiting the ability to return to N -1 reliability, but it can also help obtain an N -1-1 reliable solution.

Coordinated Operation of Concentrated Solar Power and Wind Resources for the Provision of Energy and Reserve Services

Renewable energy poses increasing challenges for power systems due to its variability, intermittency and uncertainty. The concentrated solar power ( CSP ) plant represents a promising alternative source of renewable energy technology and allows for the incorporation of thermal energy storage ( TES ) to store energy for later use. Furthermore, an electric heater ( EH ) can be integrated in CSP plant to convert supplemental energy from other sources into thermal energy for utilizations. Both TES and EH devices provide operational flexibility into power systems to accommodate more renewable energy through strategic scheduling and dispatching. Thus, we devote this paper to develop a stochastic two-stage day-ahead unit commitment model and a rolling look-ahead economic dispatch model with the integration of CSP and wind resources. We apply the proposed approach to assess the role of TES and EH in the improvement of the system's operational flexibility. Specifically, we focus on their contribution to the mitigation of the uncertainty in the outputs of renewable resources, to the reserve requirements and to the reduction of overall system costs. We perform extensive studies on a modified IEEE test system using real solar and wind data with different configurations of CSP , TES and EH . The results demonstrate the effectiveness of CSP with TES and EH in promoting the further integration of renewable energy.

Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation

One of the costs associated with integrating wind generation into a power system is the cost of redispatching the system in real-time due to day-ahead wind resource forecast errors. One possible way of reducing these redispatch costs is to introduce demand response in the form of real-time pricing (RTP), which could allow electricity demand to respond to actual real-time wind resource availability using price signals. A day-ahead unit commitment model with day-ahead wind forecasts and a real-time dispatch model with actual wind resource availability is used to estimate system operations in a high wind penetration scenario. System operations are compared to a perfect foresight benchmark, in which actual wind resource availability is known day-ahead. The results show that wind integration costs with fixed demands can be high, both due to real-time redispatch costs and lost load. It is demonstrated that introducing RTP can reduce redispatch costs and eliminate loss of load events. Finally, social surplus with wind generation and RTP is compared to a system with neither and the results demonstrate that introducing wind and RTP into a market can result in superadditive surplus gains.