Showing papers on "Power system simulation published in 2015"
TL;DR: The works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC are reviewed to help transform research advances into real-world applications.
Abstract: Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC’s birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.
519 citations
TL;DR: In this paper, a genetic algorithm-based method for sizing the energy storage system (ESS) in micro-grids is proposed to find the energy and power capacities of the storage system that minimizes the operating cost of the microgrid.
250 citations
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06 Apr 2015TL;DR: In this paper, a rigorous exposition introduces essential techniques for formulating linear, second-order cone, and semidefinite programming approximations to the canonical optimal power flow problem, which lies at the heart of many different power system optimizations.
Abstract: Optimization is ubiquitous in power system engineering. Drawing on powerful, modern tools from convex optimization, this rigorous exposition introduces essential techniques for formulating linear, second-order cone, and semidefinite programming approximations to the canonical optimal power flow problem, which lies at the heart of many different power system optimizations. Convex models in each optimization class are then developed in parallel for a variety of practical applications like unit commitment, generation and transmission planning, and nodal pricing. Presenting classical approximations and modern convex relaxations side-by-side, and a selection of problems and worked examples, this is an invaluable resource for students and researchers from industry and academia in power systems, optimization, and control.
171 citations
TL;DR: In this article, a generic mixed integer linear programming (MILP) model that integrates the unit commitment problem (UCP) with the long-term generation expansion planning (GEP) framework is presented.
169 citations
TL;DR: In this paper, the authors proposed a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs, such as electrical load, wind and solar power forecasts and generator outages.
159 citations
TL;DR: The proposed strategy addresses uncertainty using a two-stage decision process combined with a receding horizon approach that shows the appropriateness of the method to account for uncertainty in the power forecast.
Abstract: This paper presents the mathematical formulation and control architecture of a stochastic-predictive energy management system for isolated microgrids. The proposed strategy addresses uncertainty using a two-stage decision process combined with a receding horizon approach. The first stage decision variables (unit commitment) are determined using a stochastic mixed-integer linear programming formulation, whereas the second stage variables (optimal power flow) are refined using a nonlinear programming formulation. This novel approach was tested on a modified CIGRE test system under different configurations comparing the results with respect to a deterministic approach. The results show the appropriateness of the method to account for uncertainty in the power forecast.
143 citations
TL;DR: In this paper, a risk-based day-ahead unit commitment (RUC) model was proposed for the integration of wind power into the power system, which considers the risks of the loss of load, wind curtailment and branch overflow caused by wind power uncertainty.
Abstract: The integration of wind power requires the power system to be sufficiently flexible to accommodate its forecast errors. In the market clearing process, the scheduling of flexibility relies on the manner in which the wind power uncertainty is addressed in the unit commitment (UC) model. This paper presents a novel risk-based day-ahead unit commitment (RUC) model that considers the risks of the loss of load, wind curtailment and branch overflow caused by wind power uncertainty. These risks are formulated in detail using the probabilistic distributions of wind power probabilistic forecast and are considered in both the objective functions and the constraints. The RUC model is shown to be convex and is transformed into a mixed integer linear programming (MILP) problem using relaxation and piecewise linearization. The proposed RUC model is tested using a three-bus system and an IEEE RTS79 system with wind power integration. The results show that the model can dynamically schedule the spinning reserves and hold the transmission capacity margins according to the uncertainty of the wind power. A comparison between the results of the RUC, a deterministic UC and two scenario-based UC models shows that the risk modeling facilitates a strategic market clearing procedure with a reasonable computational expense.
120 citations
TL;DR: In this paper, a distributed SCUC (D-SCUC) algorithm is proposed to accelerate the generation scheduling of large-scale power systems, where a power system is decomposed into several scalable zones which are interconnected through tie lines.
Abstract: Independent system operators (ISOs) of electricity markets solve the security-constrained unit commitment (SCUC) problem to plan a secure and economic generation schedule. However, as the size of power systems increases, the current centralized SCUC algorithm could face critical challenges ranging from modeling accuracy to calculation complexity. This paper presents a distributed SCUC (D-SCUC) algorithm to accelerate the generation scheduling of large-scale power systems. In this algorithm, a power system is decomposed into several scalable zones which are interconnected through tie lines. Each zone solves its own SCUC problem and a parallel calculation method is proposed to coordinate individual D-SCUC problems. Several power systems are studied to show the effectiveness of the proposed algorithm.
113 citations
TL;DR: The results show that under certain conditions, the mobility of battery storage system can economically relieve the transmission congestion and lower the operation costs.
Abstract: This paper evaluates the effect of integrating battery-based energy storage transportation (BEST) by railway transportation network on power grid operation and control. A time-space network model is adopted to represent transportation constraints. The proposed model integrates the hourly security-constrained unit commitment with vehicle routing problem. The BEST solution provides the locational and hourly charging/discharging schedule of the battery storage system. The mobility of BEST will be of particular interest for enhancing the power system resilience in disaster areas where the transmission grid is congested or on outrage. Two cases are used to simulate the BEST including a six-bus power system linking with a three-station railway system, as well as the IEEE 118-bus systems linking with an eight-station railway system. The results show that under certain conditions, the mobility of battery storage system can economically relieve the transmission congestion and lower the operation costs.
103 citations
TL;DR: In this article, a new system planning model on a power plant resolution, taking into account technical operational constraints, is introduced, and two initial solutions are obtained; one from a classical screening curve model, and another from a model using mixed integer linear programming (MILP).
87 citations
TL;DR: The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.
Abstract: Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.
TL;DR: The proposed constraints can be used as the core of any power-based UC formulation, thus tightening the final mixed-integer programming UC problem.
Abstract: This paper provides the convex hull description for the basic operation of slow- and quick-start units in power-based unit commitment (UC) problems. The basic operating constraints that are modeled for both types of units are (1) generation limits and (2) minimum up and down times. Apart from this, the startup and shutdown processes are also modeled, using (3) startup and shutdown power trajectories for slow-start units, and (4) startup and shutdown capabilities for quick-start units. In the conventional UC problem, power schedules are used to represent the staircase energy schedule; however, this simplification leads to infeasible energy delivery, as stated in the literature. To overcome this drawback, this paper provides a power-based UC formulation drawing a clear distinction between power and energy. The proposed constraints can be used as the core of any power-based UC formulation, thus tightening the final mixed-integer programming UC problem. We provide evidence that dramatic improvements in computational time are obtained by solving different case studies, for self-UC and network-constrained UC problems.
TL;DR: In this article, two genetic algorithms were employed to determine the day-ahead microgrid scheduling and build a fuzzy expert system to control the power output of the storage system, respectively.
TL;DR: In this paper, the authors investigated and quantified the cost impact of various demand response modelings on unit commitment and dispatch in a day-ahead market regime, and showed that DR can exert downward pressure on electricity prices, causing significant implications on social welfare.
TL;DR: In this article, a new model of the unit scheduling of power systems with significant renewable power generation based on the scenario generation/reduction method combined with the priority list (PL) method is proposed that finds the probability distribution function (PDF) of a determined generator be committed or not.
TL;DR: In this paper, a stochastic security constrained unit commitment (SCUC) model for reconfigurable transmission networks is introduced and utilized to facilitate wind power integration, and the corresponding optimization problem is formulated and solved based on Benders decomposition method.
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TL;DR: In this paper, a formulation based on robust optimization where recourse decisions are approximated as linear or piecewise-linear functions of the uncertain parameters is proposed for the joint management of heat and power systems.
Abstract: The joint management of heat and power systems is believed to be key to the integration of renewables into energy systems with a large penetration of district heating. Determining the day-ahead unit commitment and production schedules for these systems is an optimization problem subject to uncertainty stemming from the unpredictability of demand and prices for heat and electricity. Furthermore, owing to the dynamic features of production and heat storage units as well as to the length and granularity of the optimization horizon (e.g., one whole day with hourly resolution), this problem is in essence a multi-stage one. We propose a formulation based on robust optimization where recourse decisions are approximated as linear or piecewise-linear functions of the uncertain parameters. This approach allows for a rigorous modeling of the uncertainty in multi-stage decision-making without compromising computational tractability. We perform an extensive numerical study based on data from the Copenhagen area in Denmark, which highlights important features of the proposed model. Firstly, we illustrate commitment and dispatch choices that increase conservativeness in the robust optimization approach. Secondly, we appraise the gain obtained by switching from linear to piecewise-linear decision rules within robust optimization. Furthermore, we give directions for selecting the parameters defining the uncertainty set (size, budget) and assess the resulting trade-off between average profit and conservativeness of the solution. Finally, we perform a thorough comparison with competing models based on deterministic optimization and stochastic programming.
TL;DR: In this paper, a mixed-integer model predictive controller for hybrid energy supply systems in buildings is presented, which is based on a hierarchical building control concept where the energy supply level is coupled to the energy consumption level only by the heat load.
TL;DR: In this article, a supercapacitor (SC) is associated as an auxiliary device with the fuel cell electric vehicle (FCEV) to ensure the power reversibility in the drive train.
TL;DR: MatACDC is the first open source program for power flow analysis of high voltage direct current grids and hybrid AC/DC systems and uses state-of-the-art developments in the field of HVDC grids research.
Abstract: This study presents the creation of a new simulation tool,MATACDC. It is the first open source program for power flow analysis of high voltage direct current (HVDC) grids and hybrid AC/DC systems and uses state-of-the-art developments in the field of HVDC grids research. MATACDC is based on MATLAB and has been fully integrated with the AC system power flow routines from MATPOWER. The software includes all the models needed to study the steady-state interaction of AC and DC systems for a wide range of converter representations and control functions. Any combination of multiple non-synchronised AC systems and multiple DC systems can be solved. The code is freely available and is intended for researchers and students working in the field of HVDC grid steady-state interactions and HVDC grid operation. MATACDC can also easily be extended with user-defined functionality. The study focuses on the program design and layout, the converter modelling, the practical implementation and the integration with AC power flow routines. Furthermore, different examples of possible user-defined functions show how the tool can be extended to include other control representations to study their effect on overall system interactions. Simulation results demonstrate the viability of the routines to simulate complex hybrid AC/DC systems.
TL;DR: In this article, a decomposition method is proposed to solve network-constrained unit commitment (NCUC) with AC power flow constraints, and feedback constraints are generated based on the sensitivity of bus voltages to a change in the unit reactive power generation.
TL;DR: In this article, the authors evaluate the effective load carrying capability of renewable resources, including wind and solar, via the stochastic long-term hourly based security-constrained unit commitment (SCUC) model.
Abstract: This paper evaluates the effective load carrying capability (ELCC) of renewable resources, including wind and solar, via the stochastic long-term hourly based security-constrained unit commitment (SCUC) model. Different from traditional approaches which approximate ELCC of renewable resources using system peak loads, nonsequential block load duration curves, or rolling-based sequential methods, the stochastic long-term hourly based SCUC could accurately examine the impacts of short-term variability and uncertainty of renewable resources as well as chronological operation details of generators on hourly supplydemand imbalance and power system reliability in a long-term horizon. Uncertainties of hourly wind, solar, and load in a 1-year horizon are simulated via the scenario tree using the Monte Carlo method, and Approximate Dynamic Programming is adopted for effectively solving the stochastic long-term hourly based SCUC model. Variability correlations between wind speed and solar radiation are considered within the scenario sampling procedure. Moreover, parallel computing is designed with the pipeline structure for accelerating the computational performance of Approximate Dynamic Programming. Numerical case studies on the modified IEEE 118-bus system illustrate the effectiveness of the proposed stochastic long-term hourly based SCUC model and the Approximate Dynamic Programming solution approach for evaluating ELCC of renewable resources. This would help independent system operators (ISO) designs effective long-term planning strategies for operating power systems efficiently and reliably.
26 Jul 2015
TL;DR: A distributed SCUC (D-SCUC) algorithm to accelerate the generation scheduling of large-scale power systems is presented, in which a power system is decomposed into several scalable zones which are interconnected through tie lines.
Abstract: Independent system operators (ISOs) of electricity markets solve the security-constrained unit commitment (SCUC) problem to plan a secure and economic generation schedule. However, as the size of power systems increases, the current centralized SCUC algorithm could face critical challenges ranging from modeling accuracy to calculation complexity. This paper presents a distributed SCUC (D-SCUC) algorithm to accelerate the generation scheduling of large-scale power systems. In this algorithm, a power system is decomposed into several scalable zones which are interconnected through tie lines. Each zone solves its own SCUC problem and a parallel calculation method is proposed to coordinate individual D-SCUC problems. Several power systems are studied to show the effectiveness of the proposed algorithm.
TL;DR: In this article, the authors investigated the sustainable day-ahead scheduling of electric power systems with the integration of distributed energy storage devices, where the main objective is to minimize the hourly power system operation cost with a cleaner, socially responsible, and sustainable generation of electricity.
13 Apr 2015
TL;DR: This paper demonstrates and discusses the integration and exchange of different (commercial as well as open source) power flow simulators with the co-simulation framework mosaik for the sake of comparing and possibly benchmarking the output of open source simulators.
Abstract: Power flow simulators are indispensible when simulating and assessing future energy system scenarios potentially comprising vast numbers of actors, devices, markets, environmental phenomena etc. While open source power flow simulators are an appealing choice – as they come free of charge – commercially available power flow simulation and optimization suites have the clear benefit of being well established and trusted by the industry. Open source implementations often lack validation against these “trusted” outputs. In this paper we will demonstrate and discuss the integration and exchange of different (commercial as well as open source) power flow simulators with the co-simulation framework mosaik for the sake of comparing and possibly benchmarking the output of open source simulators.
TL;DR: In this article, a new methodology for modeling the economic tradeoffs in implementing flexibility solutions for integrating renewables is presented, which includes both a stochastic treatment of system states to account for a wide range of operating conditions and an adapted production simulation methodology that weighs the cost of reliability and sub-hourly flexibility violations against the costs of the operational flexibility solutions available to mitigate them.
Abstract: As intermittent energy resources become more significant in power production, traditional capacity planning may be insufficient to ensure reliable system operation. A system planner must ensure that flexibility solutions are available to respond to large and uncertain ramping events. These solutions may be operational, such as improved unit commitment and dispatch, curtailment of renewables, or demand response; procurement based, such as new fast ramping resources or batteries; or involve market reform. This paper outlines a new methodology for modeling the economic tradeoffs in implementing flexibility solutions for integrating renewables. The proposed model includes both a stochastic treatment of system states to account for a wide range of operating conditions and an adapted production simulation methodology that weighs the cost of reliability and subhourly flexibility violations against the cost of the operational flexibility solutions available to mitigate them. The model's functionality is demonstrated with a case study of California at a 50% RPS in 2030. The model tests the value of 1088 MW of generic flexible units, relative to the same capacity of must-run resources, finding an expected annual value of ${\$}347\pm 42$ million/yr. Potential applications of the model for resource planning and procurement are also discussed.
TL;DR: In this article, a mixed-integer formulation with nonlinear production functions of the day-ahead hydro unit commitment problem for hydro-dominant power systems is presented to minimize unit startup and shutdown costs and maximize water use efficiency in the daily operation of multiple hydro plants.
Abstract: In this paper, we present a mixed-integer formulation with nonlinear production functions of the day-ahead hydro unit commitment problem for hydro-dominant power systems. The objective is to minimize unit startup and shutdown costs and maximize water use efficiency in the daily operation of multiple hydro plants. Determination of hourly generation dispatches and unit configurations is subject to reservoir dynamics, power balance, as well as target constraints coupling short-term generation with long-term reservoir operation goals. In addition, ac power flow constraints are considered. An equivalent quadratically-constrained quadratic formulation is approximated by successive convex semidefinite programming relaxations in a branch-and-bound algorithm for optimal solutions. Numerical case studies for several system configurations are presented to illustrate the effectiveness of the relaxation algorithm.
26 Jul 2015
TL;DR: In this article, a real-time energy management system (EMS) for a low voltage (LV) microgrid (MG) operation consists in solving the Unit Commitment (UC) and Economic Load Dispatch (ELD) simultaneously for 24 hours ahead at every 15-minute period.
Abstract: This paper proposes a real-time Energy Management System (EMS) for a low voltage (LV) Microgrid (MG). The system operation consists in solving the Unit Commitment (UC) and Economic Load Dispatch (ELD) simultaneously for 24 hours ahead at every 15-minute period. This operation is formulated as a multi-objective optimization problem where the minimization of operational cost, total emissions and power losses is simultaneously pursued using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In this algorithm, crossover and mutation operators were improved with respect to existing approaches to achieve an adequate characterization of the energy management problem and a good algorithm performance. Simulation studies have outlined that, in fact, the NSGA-II can be used as a real-time optimization tool providing a good-quality Pareto front to operate optimally the MG in a limited time of 15 minutes.
TL;DR: A holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control, which consists within a single package a three-layer enterprise control simulator which includes most of the balancing operation functionality found in traditional power systems.
Abstract: In recent years, an extensive academic and industrial literature has been developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful within the context of their application, many integration studies have methodological limitations in that they are case specific, address a single control function of power grid balancing operations, and are often not validated by simulation. This paper presents a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. It consists within a single package a three-layer enterprise control simulator which includes most of the balancing operation functionality found in traditional power systems. The control layers include a resource scheduling layer composed of a security-constrained unit commitment, a balancing layer composed of a security-constrained economic dispatch, and a regulation layer. The proposed method is validated by a set of numerical simulations. The sequel to this paper submitted to the same issue provides a set of extensive results that demonstrate how power grid balancing operations systematically address VER integration.
TL;DR: In this article, a new control strategy is presented for the day-ahead scheduling of insular power systems with a battery energy storage system, which incorporates the effects of the most relevant components such as thermal generators, wind power generation, power converter, charge controller and ESS, being integrated into the scheduling process of the system.