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The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques.

An expanded sequence-to-sequence (E- Seq2Seq) based data-driven security-constrained unit commitment (SCUC) expert system for dynamic multiple-sequence mapping samples is proposed in this paper. First,the dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input-output sequence characteristics. Then,an E-Seq2Seq approach with a multiple-encoder-decoder architecture and a fully connected extension layer is proposed. On this basis,the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct the deep learning model,and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system from Hunan province in China. The results indicate that the proposed approach possesses strong generality,high solution accuracy,and efficiency over traditional methods.

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 19712020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identied and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central no des as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research.

To ensure powersupply–demand balance under the increasing penetration of wind power, the existing nonanticipative robust unit commitment models (NRUCs) co-optimize the commitment status and the dispatch policy of power sources. Exploiting the fact that the wind power uncertainty reduces over time, this article proposes an NRUC where determining the dispatch policy is delayed until the uncertainty decreases. The proposed NRUC features three decision-making problems sequentially solved under different degrees of uncertainty. The first decision-making problem is formulated as an intractable three-stage robust optimization problem. To solve this problem, a suboptimal approach is developed where a constraint is imposed on the dispatch policy so that the transmission capacity constraint is met regardless of the dispatch level. Results of simulations on a 24-bus and a 300-bus test system show that the proposed NRUC outperforms existing NRUCs regarding feasibility and optimality under currently severe but decreasing wind power uncertainty.

This paper proposes a new model of scenario-based security-constrained unit commitment (SCUC with BESSs. By formulating such a model as a mixed-integer programming (MIP) problem, we can obtain the optimal control strategy of units and BESSs to reduce the operating cost. To solve this MIP with the proposed model, we propose a new learning-based approach to solve the security-constrained unit commitment (SCUC) problem. The proposed deep neural network (DNN)-based SCUC algorithm (DSCUC) has two main stages. First, DSCUC trains a DNN to obtain the binary unit commitment decisions. Then, the continuous variables corresponding to unit power outputs are solved by a small-scale convex optimization problem. In contrast to the existing work, the DSCUC algorithm eliminates the need of explicitly considering the scenario-based security constraints in the optimization problem, and thus greatly reduces the computational complexity. The average gap to the optimal solution is as small as 0.0964%. The algorithm is scalable in the sense that the computational time is reduced from about 1300 seconds to about 0.7 second in a 10-unit and 200-scenario system. Besides, the computation time remains almost constant when the number of scenarios increases. More than 4.70% operating cost is reduced by incorporating BESSs in the system.

Rapid growth of gas-fired generators strengthens the interdependency of electricity and natural gas markets. To meet the uncertainty challenge, a robust day-ahead market clearing framework for the integrated electricity and natural gas system (IEGS) is presented. First, a robust security-constrained unit commitment (SCUC) model is built considering the joint dispatch of ancillary services. Regulation-up/down reserve is scheduled to balance electrical load/wind power uncertainties, spinning reserve is to manage N-1 generator outage, and natural gas reserve is to withstand gas load uncertainty and provide fuel for gas-fired electrical reserve. The SCUC model is a two-stage mixed-integer nonlinear optimization problem so that a sequential linear approximation-based Column & Constraint Generation (SLA-based C&CG) algorithm is proposed. Then, according to SCUC solutions, an economic dispatch model is derived to obtain market clearing and pricing results. Based on locational marginal pricing, novel definitions of electrical energy, natural gas and multiple ancillary service prices are proposed, which can reflect network limits and uncertainty effects. Numerical cases on two test IEGSs and a provincial-level IEGS demonstrate the high accuracy of proposed SLA-based C&CG algorithm and the impact of uncertainties on market clearing.

With the rise in the penetration rate of renewable energy, the planning and operation of power systems will face huge challenges. To ensure the sufficient utilization of renewable energy, reasonable arrangement for the long-term power generation plan has become more crucial. Security-constrained unit commitment (SCUC) is a critical technical means to optimize the long-term power generation plan. However, the plentiful power sources and the complex grid structure in large-scale power systems will bring great difficulties to long-term SCUC. In this paper, we propose a fast calculation method for long-term SCUC of large-scale power systems with renewable energy. First, a method of unit statuses reduction based on temporal decomposition is proposed, which will reduce plenty of binary variables and intertemporal constraints in SCUC. Then, an efficient redundant constraints identification (RCI) method is developed to reduce the number of network constraints. Furthermore, a joint accelerated calculation framework for statuses reduction and RCI is formed, which can reduce the complexity of long-term SCUC while ensuring a high-precision feasible solution. In case studies, numerical results based on two test systems ROTS2017 and NREL-118 are analyzed, which verify the effectiveness and scalability of the proposed method.

The coordinated operation of different energy systems, such as electrical, gas, and heating, can improve the efficiency of the whole energy system and facilitate the larger penetration of renewable energy resources in the electricity generation portfolio. However, appropriate models considering various technical constraints of the energy carriers (e.g., gas system pressure limit and heat losses in the district heating networks) are needed to effectively assess the true impact of integrated energy system (IES) operation on the overall system’s performance. This article proposes a flexible unit commitment (UC) problem for coordinated operation of electricity, natural gas, and district heating networks, called multicarrier network-constrained unit commitment (MNUC), to minimize the operation cost of the IES. Besides, an integrated demand response (IDR) program is considered as a promising solution to improve consumers’ electricity, gas, and heat consumption patterns and to increase the power dispatch of combined heat and power units. Multienergy storage systems are also included in the proposed model to decrease the impact of multienergy network constraints on the overall system’s performance. To model the uncertainties involved in the operation of the three networks, a combined robust/stochastic approach is preferred in the MNUC problem considering multicarrier energy storage systems and the IDR program. Numerical results show that the whole operation cost of the IES has decreased by 2.58% considering the IDR program and multienergy storage systems.

Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver.

Currently electricity markets worldwide encounter a transition phase into cleaner energy. This is specially the case in Greece, where not only the market structure changes to a harmonized EU target model, but also ‘green electricity’ is projected to reach 50% share by 2030 according to the National Energy and Climate Plan of Greece. This paper presents the extension of the Business Strategy Assessment Model (BSAM) into a computationally efficient central-dispatch wholesale market model. BSAM integrates agent-based profit-maximizing policy making with unit commitment scheduling, into one novel tool that can be used to explore the effects of mid-term market design policies in wholesale electricity markets. BSAM is subsequently utilized to model the Greek market’s transitional phase and explore the effects of even higher RES penetration scenarios. Results indicate that the planned short-to-mid-term “de-lignification” and transition of the Greek power system is feasible and will not lead to an excessive RES curtailment situation, but would yield significant CO2 emissions’ reductions and small reductions in the system marginal price, with even more ambitious RES shares also being possible.

The integration of the variable renewable energies makes the operation conditions of the power system ever-changeable. Consequently, the power system operational reliability evaluation is increasingly important. This paper introduces the concept of decision-dependent uncertainty (DDU) in the operational reliability evaluation. Unlike the exogenous uncertainties, DDU reveals that the decisions of the system operation could significantly affect the resolution of the uncertainties which influence the reliability metrics. In this paper, the proposed DDU modeling method links the device reliability indices, i.e., the forced outage rate, and the operational-decision variables. The impacts of DDU on operational reliability are analyzed based on a reliability-constrained stochastic unit commitment (UC) model. An adaptive reliability improvement UC (ARIUC) algorithm is proposed to efficiently solve the problem. Case studies underline the necessity of considering DDU in power system operational reliability evaluations.

Bulk integrated electricity and gas systems (IEGSs) introduce complex coupling relations and induce synergistic operation challenges. The growing uncertainty arising from the renewable power generation in the IEGS further aggravates the synergistic problems. Considering the availability of historical wind power generation data, this paper adopts a two-stage sample robust optimization (SRO) model, which is equivalent to the two-stage distributionally robust optimization (DRO) model with a ${\text{type}} - \infty $ Wasserstein ambiguity set, to address the wind power penetrated unit commitment optimal energy flow (UC-OEF) problem for the IEGS. Compared to the equivalent DRO model, the two-stage SRO model can be approximately transformed into a computationally efficient form. Specifically, we employ linear decision rules to simplify the proposed UC-OEF model. Moreover, we further enhance the tractability of the simplified model by exploring its structural features and, accordingly, develop a solution method. Simulation results on two IEGSs validate the effectiveness of the proposed model and solution method.

High penetration of photovoltaic (PV) power generation systems is restricted due to the risk of a critical stability decline of the power system. The stability decline has been caused by the power supply/demand imbalance due to the mismatch between photovoltaic power generation and load demand, and exceedance of generators ramp up/down rate with netload variation. This risk is known as the “duck curve problem” in the planning phase. Measures have been proposed, such as demand response (DR), energy storage system (ESS) management, and curtailment of PV generation to mitigate this problem. In this paper, a bifacial PV system of the vertical installation is proposed for duck curve mitigation, which is the simplest and unprecedented approach for the power system. The bifacial PV module’s detailed irradiance model revealed that the proposed method could simultaneously achieves the three characterized peaks; low peak, shifted peak, and bimodal peak. The effect of three characterized peaks on the power system is verified by solving the unit commitment (UC) problem. Besides, the synergistic effect with the ESS management is also verified by the UC problem. Moreover, a multi-objective approach has been applied to the UC problem to assess the proposed PV system and ESS design. Finally, the proposal was clarified that advantages in points of PV installation and total PV output are increased to + 34 % and 21% respectively, daily operation costs are saved by 2.3%, and reduction of required ESS capacity is achieving to − 94 % by short cycle charge/discharge operation. This result is concluded that the proposal is in spite of a very simple method, it clarifies the remarkable effects of power system stability, economic advantage, and the possibility of high penetration of renewable energy.

Energy storage unit (ESU) is playing an increasingly important role in load shifting and uncertainty mitigation. This paper aims to quantify the value of ESU in the unit commitment (UC) with renewable generation. By treating the power and energy capacities of ESU as continuous parameters, the stochastic UC problem is cast as a multi-parametric mixed-integer linear program (mp-MILP), whose optimal value function (OVF) gives the relation between storage capacity and the daily UC cost in an analytical manner. It encompasses abundant sensitivity information, and its surface can be easily visualized. The reduced cost compared with the benchmark case without storage can be regarded as the value of ESU. As a potential application, the OVF is used to formulated an optimal storage sizing problem that maximizes the ratio between the reduced operation cost and the investment cost, ensuring the minimum time of cost recovery. The solution consists of two steps: the first step constructs the OVF parameterized in storage capacity; the second step reformulates the fractional storage sizing program into an MILP, leveraging the expression of the OVF and variable transformations. Case studies conducted on the IEEE 9-bus system and IEEE 118-bus system illustrate how the OVF offers useful reference for power system operation and planning. The proposed optimal sizing model shows advantages in efficient utilization of investment in terms of payback time and rate of return.

The development over the last decade of the district heating (DH) network of Gleisdorf (Austria) represents an innovative example for a transition to a 4th generation DH network (4GDH). The local DH operator has strong ambitions towards implementing a highly efficient, nearly 100% renewable DH system using local resources. These goals will be achieved by the development and implementation of a novel control strategy, namely a “Virtual Heating Plant” (VHP) control concept. The VHP concept is an intelligent high level control system using operating data for real time calculations and a 24h prediction to optimise the overall DH network. The VHP control is fully implemented into the DH system and led to an improved and more flexible unit commitment, higher solar yields and more stable operating conditions. This paves the way to integrate multiple decentralised local heat sources and existing boiler units as well as other systemic approaches (e.g. sector coupling with waste water treatment plant, cascading heat supply out of the return pipe, stepwise system temperature reduction, implementation of an intelligent, decentral storage concept) and non-technical measures (e.g. user and stakeholder integration).

The replacement of directly connected synchronous generators with power electronics interfaced generation has led to a decrease in system's inertia posing a significant challenge on frequency dynamics. In isolated systems with reduced inertia predefined limits for renewable penetration and primary reserves are frequently set for dynamic security purposes. This approach might not ensure dynamic security or can prove conservative in certain conditions. Furthermore, these approaches rarely consider the capabilities of inverter based renewable generation to provide frequency services. In this paper, a data driven approach, based on optimal classification trees is proposed to extract, from a detailed dynamic model of the system, the constraints for a frequency dynamic unit commitment formulation. Hence, both dynamic security and optimal exploitation of renewable and conventional units for power production and frequency support can be achieved. The advantages of the proposed method compared to conventional and state of the art approaches in frequency security are validated through dynamic simulations on a realistic model of Rhodes island and IEEE 118. Uncertainties in load demand and renewable generation are dealt by a robust optimization method. Its economic performance, computational overhead and modelling complexity is compared to a stochastic approach.

With the increasing integration of renewable energy sources (RESs) and the implementation of dynamic line rating (DLR), the accompanying uncertainties in power systems require intensive management to ensure reliable and secure operational planning. However, while numerous approaches and methods in the literature deal with uncertainties, they have not been analyzed axiomatically. This paper presents an analysis of risk in power system operation using coherent risk measures, elaborating on the origin of risk and the mechanisms of its management in the presence of various sources of uncertainty. To illustrate the practicality and benefits of coherent risk measures, a risk-averse asymmetry robust unit commitment (UC) model is established. It is based on coherent reformulations of the uncertain reserve and line flow constraints and is formulated in the form of a compact computationally efficient mixed-integer second-order conic program (SOCP). The overall performance of the proposed framework is verified using the updated 2019 IEEE Reliability Test System and the ACTIVSg2000 test system over a year-long period.

Coronavirus disease 2019 (COVID-19) has affected many behaviours and aspects of society. Electricity consumption has been considerably affected by the pandemic, with significant effects on the electricity load demand profile. In this article, the impact of COVID-19 on electricity demand in the state of Florida is investigated through a novel machine learning technique. The LSTM technique shows good accuracy in forecasting the load profiles for all days studied (weekdays and weekends) and also before and during the pandemic. The UC problem is solved considering the load profiles, and the impact of COVID-19 on power plant scheduling is evaluated. The simulation results show an increase in residential demand for electricity at weekends, while both residential and commercial demand are reduced during weekdays. Therefore, the operating cost of a weekday in 2020 was lower than that in 2019, while the operating cost of a weekend was higher in 2020 than in 2019. [ABSTRACT FROM AUTHOR] Copyright of Engineering Optimization is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Meeting the customer’s power demands is crucial for energy companies, even when unexpected and consecutive failures are present. This task has considerably increased its complexity due to the high integration of renewable energies and their intermittent behaviors. Therefore, it is important to achieve reliable power supply based on a criterion closer to real-life system operations and capable of addressing consecutive failures. The N-1-1 contingency involves the loss of a single transmission line or generation unit, followed by systems adjustments. Afterward, the power system experiences a subsequent loss of an additional generation unit or transmission line. This paper presents a power system unit commitment problem considering the N-1-1 reliability criterion with operations compliance check on economic dispatch and power flows under contingency states and renewable energy integration. Corrective actions are also included to determine the time that the failed components are restored. To address the complexity caused by renewable energy integration, the reliable unit commitment is achieved under the worst-case renewable output. The formulation results in an extremely large-scale adaptive robust mixed-integer linear programming model. For an efficient solution, a variation of the nested column-and-constraint generation algorithm is designed. Besides using the susceptance and phase angles to model the power flow, the linear sensitivity factors are also applied for improving the computational performance. The proposed models and algorithms are evaluated on modified IEEE 6-bus, 14-bus, and 118-bus test systems to confirm their effectiveness.

This paper proposes a resilient and secure configuration for coastal distribution grids by integrating the security constraint unit commitment (SCUC) and mobile marine microgrids (MMMGs). In the proposed configuration, MMMGs can be connected to the coastal distribution grids in both normal and post-disaster operations. It is assumed that both MMMGs and SCUC networks include both dispatchable (e.g., gas turbines and diesel generators) and nondispatchable generators (e.g., photovoltaics and wind turbines). The proposed problem consists of realistic formulations that seek to minimize the total MMMGs and SCUC operation costs, while maximizing distribution grid resiliency. A heuristic technique, known as the collective decision optimization algorithm, is employed to address the complexity and nonlinearity of the formulated problem. Moreover, the unscented transform technique is adopted to model the uncertainties associated with renewable energy sources output and load demand. To show the effectiveness and merits of the proposed configuration, the IEEE 69-bus distribution network is selected and tested for both normal and post-disaster operations.

Recently, the European Commission passed the Guideline on Electricity Balancing to standardize future reserve products in European electricity markets. Strict regulatory requirements are imposed on ramping behavior that must be adhered to, which brings ramping constraints into renewed focus. In a literature review, we find that prevalent ramping constraint formulations cannot guarantee that those regulatory requirements are fully satisfied. Since reserve activation in relation to ramping constraints is not discussed in literature, we aim to fill this research gap with a focus on the impact of reserve activation on ramping feasibility, availability of reserve, and thereby induced imbalances. We argue for the use of a simple, yet consequential and more robust, extended version of ramping constraints to account for intertemporal dependencies of reserve in general and to satisfy regulatory requirements in particular. Prevalent formulations significantly overestimate available reserve compared to the robust formulation. Worst-case bounds on the overestimation factor are provided for common operating modes. Computational experiments on standard benchmark sets confirm that the prevalent formulation overestimates reserve in realistic scenarios. Reserve activations are simulated from German data in a comprehensive ex-post analysis to identify reserve overestimation as a hitherto neglected source of imbalances and to quantify its extent.

The convergence of the electric power industry toward the competitive framework throughout the world has led to a market environment replacing the traditional monopolistic scenario. It has given rise to more efficient production and distribution of electricity, provided a better choice to market participants while maintaining the security and reliability of the power supply. Earlier power utilities had been producing electrical power with the objective to minimize the costs and demand, as well as reserves, were met. Under the new structure, generation companies (GENCOs) schedule their generators to maximize profit. This paper uses a new approach to deal with profit-based unit commitment (PBUC), considering the conditions of power and reserve. The proposed method allows GENCO to decide how much power and reserve should be sold in the markets to achieve maximum profit. For the PBUC problem, three computational techniques, namely ANTIGONE, COUENNE, and BARON in general algebraic modeling system (GAMS), have been applied on three test case studies comprising three, ten, and hundred thermal units in a day-ahead power market. The uncertainty in the energy spot price has been considered using Monte Carlo simulation approach. The various diversified constraints such as system energy load, spinning reserve, ramp rate, unit up and downtime imposed on the PBUC problem have been considered while solving the problem. Simulation results demonstrate that the BARON method has provided better results and has outperformed over the ANTIGONE and COUENNE methods and previously existing techniques reported in the literature in terms of solution quality, the satisfaction of constraints, and an ability to solve large-scale test system.

The production cost modeling (PCM) plays an important role in the mid/long-term power system planning. With the increasing penetration of renewable power, it becomes solving a series of unit commitment (UC) problems for time periods up to one or multiple years. This process is greatly challenged by the UC solution efficiency which hinders its practical use. In this paper, a fast UC method is proposed based on the load state transition curve (LSTC). Firstly, LSTC is proposed and obtained via load clustering. It simplifies the load model as well as maintains the necessary temporal characteristics. Secondly, the LSTC based UC model is developed by accommodating the constraint formulation to LSTC. Compared with the conventional ones, the scale of LSTC-based UC is dramatically reduced. The stochastic and volatile wind power is also taken into consideration. The case studies on the modified IEEE-RTS 1979 have verified the validity and feasibility of the proposed method in which the solution time is reduced from ∼300s to ∼12s with the cost error lower than 0.2%. It provides an efficient tool for the optimal planning and operation of large-scale power systems.

Optimizing the hydro unit commitment (HUC) has the potential to increase water use efficiency. However, the large number of variables, along with some nonlinear and non-concave constraints, present a huge challenge to the efficient solution of the HUC problem. For hydropower stations including multiple power plants that have different hydraulic heads, the problem is even more difficult. In this paper, we address this complex HUC problem using a two-layer nested optimization method. First, the total load demand is allocated to each power plant using a novel distribution rate. Thus, in the outer layer, a cuckoo search algorithm optimizes the distribution rate and each unit’s on/off status, while in the inner layer, the load demand of each plant is allocated to online units through dynamic programming. China’s Gezhouba Hydropower Station is selected for a case study. It is shown that: (1) the optimized water consumption is, on average, 1.71% lower than that of actual operations; (2) the optimized status of units vary smoothly with fewer startup/shutdown events; and (3) the optimization performance in non-flood seasons is better than that in flood seasons. These findings verify that the two-layer nested optimization method is an effective means of solving HUC problems for hydropower stations including multiple hydro plants with different hydraulic heads.

Recently, the increasing penetration of wind energy has provided various ways to operate the system at low cost and low pollution. On the other hand, the noticeable uncertainties caused by the forecast error of wind speed restrict the utilization of wind energy. Moreover, it brings unprecedented challenges to maintain system adequacy. One prevailing solution is to maintain sufficient spinning reserve of thermal units, which is not affordable. This paper analyses the relationship between the operation cost and wind power uncertainty using AC unit commitment in the frame of a short-term market. Conditional value-at-risk is established in a percentage of the worst-case estimations on wind power scenario under uncertainty to achieve adequate trade-offs between optimal solution and risk aversion. Operation cost of generation, load shedding cost and reserve cost are optimally established by multi-objective optimization. Finally, the simulation is performed on the test system and the obtained results are evaluated and compared to demonstrate the research effectiveness.

Purpose of ReviewAs the application space for energy storage systems (ESS) grows, it is crucial to valuate the technical and economic benefits of ESS deployments. Since there are many analytical tools in this space, this paper provides a review of these tools to help the audience find the proper tools for their energy storage analyses.Recent FindingsThere are many software tools for valuating ESS. These tools can be classified into two groups: (1) power system simulation and planning tools for analyzing the technical contributions of ESSs, and (2) techno-economic analysis tools for valuating the economic benefits of ESS deployment and specifying the optimal design of energy systems that include ESSs. While many of the tools, developed by the national laboratories, are free to use, the commercial tools are also of great importance in meeting the customers’ specific needs.SummaryThis paper provides a review of software tools for ESS valuation and design. A review of analysis tools for evaluating the technical impacts of energy storage deployments is also provided, as well as a discussion of development trends for valuation and design tools.

Block orders are increasingly adopted in many electricity markets as they enable power producers to implicitly express their unit commitment characteristics and address price volatility risks. However, previous work on the optimal offering problem of a price-taking producer participating in such markets has only considered the simplest type of block orders (regular block orders-RBO) and has neglected relevant realistic market regulations. This paper addresses these simplifications by proposing a new optimal offering model which also considers the more complex profile block orders (PBO) and linked block orders (LBO). This model is generic in that it can treat the block orders time spans either as fixed parameters (following the modelling approach of previous work) or as endogenous decision variables, which constitutes a completely novel modeling approach. The presented case studies demonstrate that this novel approach yields a tremendously lower number of binary variables and more efficient computational performance, when PBO and LBO are considered. Furthermore, the case studies analyze the physical conditions under which the introduction of different types of block orders increases the examined producers profit.

The information and communication technology (ICT) is increasingly involved in the measurement, operation, control and protection of the cyber-physical power system (CPPS). As one of the most promising communication infrastructure projects, Starlink is a satellite Internet constellation being constructed by SpaceX for providing global Internet access, which can benefit the network service supply for the communication-enabled power system equipment in locations where the network access is unreliable, expensive, or completely unavailable. In this letter, the future applications of the Starlink space network in CPPSs are explored: the communication infrastructure and transmission parameters are discussed, and the corresponding test case was emulated in real-time on the heterogeneous co-emulation platform to validate the proposed concept of space network enhanced cyber-physical power system.

Due to the absence of utility power grid infrastructure in remote military bases, on-site diesel generators serve as the primary sources for power demands. Increasing efficiency and preventing frequent startup/shutdown operations of on-site diesel generators are therefore becoming a critical issue for reducing fuel cost. Application of vehicle-to-grid technology in a military-based microgrid embodies potential for significant fuel economy benefits since on-board vehicle generators and energy storage units can serve as mobile power sources that provide higher flexibility for supplying power demands. In addition, energy storage system integration is considered as an alternative solution for increasing on-site diesel generators efficiency and lessening their startup/shutdown operations. This article proposes a three-stage planning procedure for identifying the optimal locations and capacities of energy storage systems, considering multiple operating scenarios via stochastic programming. Note that on-site diesel generators and on-board vehicle generators support plug-and-play functionality, meaning their startup/shutdown operations can be decided in real time. Furthermore, network-constrained ac unit commitment model is used to optimize operation of microgrids. It is assumed that in the tested microgrid systems, several tactical military vehicles with on-board generators and energy storage units are deployed as alternative power sources. The economic merits of vehicle-to-grid implementation and energy storage system integration in a military-based microgrid are validated in the numerical studies.

The power system planning requires the simulation of unit commitment (UC) for long time periods up to multiple years or decades which is conducted with production cost modeling (PCM) and usually ends up solving a number of mixed integer programming problems. The development of concentrating solar power (CSP) raises the computational burden of PCM dramatically due to its complex structural and operational characteristics, small unit capacity and large unit number. To improve the PCM efficiency for power systems with large scale CSP generation, a CSP clustering method is proposed based on a novel CSP unit model where the startup and heat accumulation process is considered to simulate CSP operation properly and avoid infeasible operation schemes. In the CSP clustering method, a series of CSP units are modelled in clusters and the numerous binary variables are replaced by integer ones. The number of the variables can be greatly reduced and so is the problem scale. The case studies on the modified IEEE-RTS 1979 have verified the validity and effectiveness of the proposed model and method. The CSP clustering method well outperforms the aggregated unit model which is introduced for comparison. The solution time for the UC with CSP is dramatically reduced (even by 1–2 orders of magnitude) with high accuracy especially the generation cost whose deviation is usually below 0.1%. The proposed method shows great potential in power system planning and operation with large-scale CSP generation.

Coordination efficiency between different energy sectors with privacy preservation has become a technical bottleneck in distributed operation of the integrated electricity and heat systems (IEHS). This article investigates, for the first time, an equivalent model-based non-iterative solution for this issue. An equivalent model of district heating networks (DHNs) retaining the temperature quasi-dynamics is derived. Based on the equivalence, all the state variables in DHNs are explicitly expressed by the heat power generation, and the feasible region of DHNs is projected on the coupling boundaries of the IEHS. The existence of this equivalent model is proved under a mild sufficient condition. The application of the proposed equivalent model is demonstrated via the distributed unit commitment for IEHS. Case studies of two test systems validate the effectiveness and the efficiency of the solution for distributed IEHS operation.

Renewable energy (RE) integration into the power grid is steadily growing that effectively reducing the electricity generation cost, however, leads to additional consequences. Unit commitment (UC) problem often cited in discussing these issues. This paper proposes an approach to look at these issues by utilizing the Multi-agent Immune incorporating Evolutionary Priority List (MAI-EPL) technique for solving the UC problem with the consideration of RE resources, as an effort to reduce the operation cost without affecting the existing power system reliability. The employed test systems consider 10 thermal generating units with additional solar and wind resources in a 24-hours scheduling period. The results show that the MAI-EPL technique is capable of producing a satisfactory outcome to the UC problem with RE consideration.

In contrast to most existing works on robust unit commitment (UC), this study proposes a novel big-M-based mixed-integer linear programming (MILP) method to solve security-constrained UC problems considering the allowable wind power output interval and its adjustable conservativeness. The wind power accommodation capability is usually limited by spinning reserve requirements and transmission line capacity in power systems with large-scale wind power integration. Therefore, by employing the big-M method and adding auxiliary 0-1 binary variables to describe the allowable wind power output interval, a bilinear programming problem meeting the security constraints of system operation is presented. Furthermore, an adjustable confidence level was introduced into the proposed robust optimization model to decrease the level of conservatism of the robust solutions. This can establish a trade-off between economy and security. To develop an MILP problem that can be solved by commercial solvers such as CPLEX, the big-M method is utilized again to represent the bilinear formulation as a series of linear inequality constraints and approximately address the nonlinear formulation caused by the adjustable conservativeness. Simulation studies on a modified IEEE 26-generator reliability test system connected to wind farms were performed to confirm the effectiveness and advantages of the proposed method.

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.

Wind and solar as two renewable energy resources are largely used to generate clean and sustainable energy in the power systems. To integrate these renewable energies in the power system, different aspects of the power system such as reliability and operation are affected that must be investigated. It is due to the variation in the generated power of these resources that are arisen from the variation in the wind speed and solar radiation. To model the uncertainty nature of large-scale wind and photovoltaic farms in the operation studies of the power system, an appropriate multistate reliability model is developed for the wind and photovoltaic farms considering both failure of main components and variation in the wind speed and solar radiation. To determine the optimum number of states in the reliability model of wind and photovoltaic farms, XB index is calculated and based on the fuzzy c-means clustering technique the transition rates among different states are obtained. To calculate the transition rates among different states, the fuzzy numbers are used that results in the accurate and reduced reliability model for wind and photovoltaic farms. To determine the probability of different states in the reliability model of wind and photovoltaic farms in the operation studies, matrix multiplication technique is utilized to determine important indices of the power system such as the unit commitment risk. In this paper, the amount of required spinning reserve of a power system containing wind and solar generation units can be determined based on the reliability criterion. The proposed technique is applied to the two reliability test systems including RBTS and IEEE-RTS and the reliability-based operation indices of these systems are calculated to present the effectiveness of the proposed analytical method.

The Unit Commitment (UC) is a significant act of optimization in day-to-day operational planning of modern power systems. After load forecasting, UC is the subsequent step in the planning process. The electric utilities decide in advance which units are to start-up, when to connect them to the network, the sequence in which the generating units should be shut down and for how long. In view of the above, this paper attempts on presenting a thorough and precise review of the recent approaches applied in optimizing UC problems, incorporating both stochastic and deterministic loads, based on various peer reviewed published research papers of reputed journals. It emphasizes on non-conventional energy and distributed power generating systems along with deregulated and regulated environment. Along with an overview, a comprehensive analysis of the UC algorithms reported in the recent past since 2015 has been discussed for the assistance of new researchers concerned with this domain.

The high penetration of wind generation has prompted the development of stochastic hydrothermal unit commitment (SHTUC) models, which are more difficult to be solved than their thermal-based counterparts due to hydro generation constraints and inflow uncertainties. For handling the uncertainty, the problem is usually formulated as a two-stage stochastic model (2S-SHTUC), although multistage (MS-SHTUC) formulations have gained increasing attention due to their more realistic assumptions about on–off decisions over the planning horizon. Benders decomposition (BD) is one of the most common methodologies used for solving 2S-SHTUC and MS-SHTUC problems. To overcome the well-known slow convergence of the classical BD when applied to large problems, most authors use accelerating techniques. In this paper, we implement state-of-the-art stabilization methods tailored for speeding up the convergence of the classical BD: local branching and the level regularization. Our experiments are conducted in a real-life SHTUC problem with 11 thermal units, 16 hydro plants, 46 buses and 95 lines. The results show that 2S and MS-SHTUC can benefit from stabilization. The savings in computing times range from 69 to 95% for the 2S-SHTUC model and from 77 to 89% for the MS-SHTUC.

When the correlation of wind power output among wind farms is not considered, the integrated stochastic characteristics of wind power will not be captured accurately. Using this inaccurate feature may lead to an impractical even a failing result of unit commitment (UC). Therefore, this paper proposes a multiple correlations model for wind power forecast errors (WPFEs), and to capture this multiple correlation feature in UC problem, a two‐stage chance‐constrained interval UC (CIUC) model is proposed. First, an analytical expression of multiple correlations, including spatial, temporal and conditional correlations, is presented to improve the description accuracy of stochastic WPFEs. To strike a balance between risk and operational cost, a chance‐constrained decision method is developed to optimize the time‐varying interval of wind power output in the first stage. Subsequently, an interval UC model is established to determine the optimal operational schedule in the second stage. Finally, the proposed CIUC model is solved using a solution strategy that combines column‐and‐constraint generation and sample average approximation. The effectiveness and practicality of the proposed method are verified via the numerical results for IEEE 39‐bus and 118‐bus systems.

The integration of highly intermittent and unpredictable distributed energy resources increases the complexities in the energy management system (EMS). Therefore, the conventional control used in m...

This paper proposes a novel hierarchical and robust scheduling paradigm for the emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid system with high share of wind power. Considering the multilevel structure of the VSC-MTDC meshed AC/DC system, the scheduling problem is decomposed into three interactive levels according to the hierarchical analytical target cascading (ATC) technique. The low level is a two-stage adaptive robust security-constrained unit commitment problem for the AC system, which is solved by the column-and-constraint generation (CC the middle level is a day-ahead power transmission optimization problem for the VSC stations, where a day-ahead operation model for the VSC station is presented to fully use its flexible and controllable power adjustment capability to promote wind power accommodation; and the high level is a multi-period optimal power flow problem for the DC grid. An integrated ATC and C&CG algorithm is proposed to enable the hierarchical and robust scheduling. The parallel solution is used to accelerate the hierarchical scheduling. Simulations show that thisformulation is effective in dealing with wind power uncertainty, promoting wind power accommodation, and improving the calculation efficiency.

Stochastic network-constrained unit commitment (S-NCUC) can be used to manage the uncertainty of an increasing penetration level of renewable energy effectively. However, the drawbacks of the progressive hedging algorithm (PHA) based solutions are that they are not provably convergent due to the non-convexity of S-NCUC. Additionally, the solution obtained is usually fractional-valued (non-binary) and therefore not readily implementable. In this paper, we apply a novel Penalty-Based Gauss-Seidel (PBGS) algorithm in solving S-NCUC using an exact augmented Lagrangian representation with proven convergence. To improve the computational efficiency of the PBGS, we further propose an accelerating technique with rigorous proof to skip solving scenarios when certain conditions are met during iterations. We numerically validate the proposed algorithms on the IEEE 118-bus system and a practically-sized ERCOT-like system, both with variable wind generation. Numerical results demonstrate the efficacy of the proposed algorithms in yielding high-quality S-NCUC solutions. The merits of the proposed algorithms are also revealed in comparison with PHA and extensive-form (EF) based mixed-integer programming solutions.

Owing to the economic, social, and political problems in the expansion of transmission infrastructure, novel transmission topologies are in demand to efficiently utilize the existing infrastructure. On the other hand, there is a tremendous increase in wind power generation around the globe. One of the main challenges hindering the penetration of large-scale wind power generation is the network congestion due to limited network capacity. To address these two issues, this study develops a co-optimized optimal transmission switching (OTS) and dynamic line rating (DLR) model to optimize system resources by mitigating network congestion and maximizing wind power accommodation. This new concept of exploiting the inherent flexibility in transmission network is named as flexible transmission dynamic line rating (FTDLR), which deploys OTS in coordination with DLR as a control tool to utilize existing assets. A two-stage stochastic unit commitment framework is used to deploy the proposed FTDLR model, which is used to dynamically increase the line capacity based on the meteorological parameters and at the same time optimally select candidate lines to be switched off from the network. A comprehensive analysis is performed to characterize the FTDLR performance on system operation cost, network congestion, and wind power curtailment. The proposed FTDLR model is further tested as a part of contingency analysis where both generator failure and transmission line outages are considered. Test results performed on the IEEE 24-bus network demonstrate that by using FTDLR, the service operator could substantially reduce system dispatch cost, improve wind power accommodation, and relieve network congestion. The scalability and feasibility of the FTDLR optimization problem is validated on the larger network of the IEEE 118-bus system.

The main objective of the Security Constrained Hydrothermal Unit Commitment (SCHTUC) is the optimal committing of the hydro and thermal units i.e., to minimize the total cost (which are very non-linear and non-convex) of thermal plants while satisfying the many hydro-thermal constraints. Such constraints, together with the nonlinear non-convex and mixed-integer objective function, make the search space extremely complex. The time of operation of the units is considered to be 24 h. To achieve this objective, a Genetic Algorithm (GA) with two new constraint handling repair mechanisms was applied. The proposed algorithm will be applied to the hydrothermal system, under different hydrological conditions, to prove i.e., confirmation of the initial hypothesis, which indicates that the total operating costs depend significantly on the hydrological conditions, i.e., the available volume of water and the inflows in the reservoirs of the hydropower plants. The proposed algorithm was applied on IEEE 30 bus system, but also tested on a benchmark system, and confirmed by comparison with other hybrid techniques such as PSO–GWO and DA-PSO. The efficiency of the proposed algorithm is established by comparing it to these two hybrid algorithms.

Coordinately embedding machine learning and optimization in the Monte Carlo simulation is proposed as a new framework for reliability evaluation, which is becoming more challenging due to the joining of variability and intermittency of the renewable generation and components’ ageing. As a machine learning method, the dynamic Bayesian belief network predicts the renewable outputs by generating their probability distributions through historical data to overcome the defect of rarely grasping the low-probability events in the traditional methods, which either predict a single-point value or presume a parametric probability distribution. As the internal operation module of the framework, the rolling-horizon unit commitment maintains the complexity of the operation model and meantime punctually updates the predicted renewable generation and the components’ ageing. The dynamic Bayesian belief network and the rolling-horizon unit commitment traverse time step after time step throughout the horizon, and thus form one bid of the Monte Carlo simulation. In each time step, the dynamic Bayesian belief network extracts the probability distributions of the renewable generation, from which the boundary of the commitment is sampled, and the impact of scheduling the generators on their ageing is then accumulated from the commitment. The proposed framework has its effectiveness proved on a microgrid.

In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation. Precise forecasting of load is essential for unit commitment, capacity planning, network augmentation and demand side management. Load forecasting can be generally categorized into three classes such as short-term, midterm and long-term. Short-term forecasting is usually done to predict load for next few hours to few weeks. In the literature, various methodologies such as regression analysis, machine learning approaches, deep learning methods and artificial intelligence systems have been used for short-term load forecasting. However, existing techniques may not always provide higher accuracy in short-term load forecasting. To overcome this challenge, a new approach is proposed in this paper for short-term load forecasting. The developed method is based on the integration of convolutional neural network (CNN) and long short-term memory (LSTM) network. The method is applied to Bangladesh power system to provide short-term forecasting of electrical load. Also, the effectiveness of the proposed technique is validated by comparing the forecasting errors with that of some existing approaches such as long short-term memory network, radial basis function network and extreme gradient boosting algorithm. It is found that the proposed strategy results in higher precision and accuracy in short-term load forecasting.

Large-scale renewable energy integration decreases the system inertia and restricts frequency regulation. To maintain the frequency stability, allocating adequate frequency-sup-port sources poses a critical challenge to planners. In this context, we propose a frequency-constrained coordination planning model of thermal units, wind farms, and battery energy storage systems (BESSs) to provide satisfactory frequency supports. Firstly, a modified multi-machine system frequency response (MSFR) model that accounts for the dynamic responses from both synchronous generators and grid-connected inverters is constructed with preset power-headroom. Secondly, the rate-of-change-of-frequency (ROCOF) and frequency response power are deduced to construct frequency constraints. A data-driven piecewise linearization (DDPWL) method based on hyperplane fitting and data classification is applied to linearize the highly nonlinear frequency response power. Thirdly, frequency constraints are inserted into our planning model, while the unit commitment based on the coordinated operation of the thermal-hydro-wind-BESS hybrid system is implemented. At last, the proposed model is applied to the IEEE RTS-79 test system. The results demonstrate the effectiveness of our co-planning model to keep the frequency stability.

Contingency analysis is an important building block in the stability and reliability analysis of power grid operations. However, due to the large number of transmission lines, in practice only a limited number of contingencies could be evaluated. This paper proposes a graph theory based $N$-1 contingency selection method to effectively identify the most critical contingencies, which can be used in security-constrained unit commitment (SCUC) problems to derive secure and economic operation decisions of the power grid. Specifically, the power flow transferring path identification algorithm and the vulnerability index are put forward to rank individual contingencies according to potential transmission line overloads. Effectiveness of the proposed $N$-1 contingency selection method is quantified by applying the corresponding SCUC solution to the full $N$ - 1 security evaluation, i.e., quantifying total post-contin-gency generation-load imbalance in all $N$ -1 contingencies. Numerical results on several benchmark IEEE systems, including 5-bus, 24-bus, and 118-bus systems, show effectiveness of the proposed method. Compared with existing contingency selection methods which usually resort to full power flow calculations, the proposed method relies on power gird topology to effectively identify critical contingencies for facilitating the optimal scheduling of SCUC problems.