Showing papers on "Power system simulation published in 2021"
TL;DR: A min max min robust framework for short-term operation of microgrids with natural gas network to capture the uncertainty of wind generation and electrical/thermal loads and immunizes against all realizations of uncertainties is leveraged.
64 citations
TL;DR: A holistic structure to determine the optimal coordinated operation of the grid-connected energy hubs and the regional power system by relying on the high penetration of wind power is proposed and can be seen that the proposed strategy is a very effective step towards achieving a 100% renewable energy system.
59 citations
TL;DR: A new two-stage hybrid stochastic–information gap-decision theory (IGDT) based on the network-constrained unit commitment framework is proposed for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems.
Abstract: This article proposes a new two-stage hybrid stochastic–information gap-decision theory (IGDT) based on the network-constrained unit commitment framework. The model is applied for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems. The uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. The proposed model considers both risk-averse and risk-seeker strategies, which enables the independent system operator to provide flexible decisions in meeting system uncertainties in real-time dispatch. Moreover, the effect of feasible operating regions of the combined heat and power (CHP) plants on energy and flexible ramping reserve market and operation cost of the system is investigated. The proposed model is implemented on a test system to verify the effectiveness of the introduced two-stage hybrid framework. The analysis of the obtained results demonstrates that the variation of heat demand is effective on power and flexible ramping reserve supplied by CHP units.
58 citations
TL;DR: A hybrid information gap decision theory (IGDT)- stochastic method to solve a transmission-constrained AC unit commitment model integrated with electric vehicle (EV), incentive-based DRP, and wind energy is proposed.
58 citations
TL;DR: In this paper, a simplified LSTM algorithm built over the architecture of Machine Learning methodology to forecast one day-ahead solar power generation is introduced, which can successfully capture intra-hour ramping on different weather scenarios.
Abstract: In recent years, exploration and exploitation of renewable energies are turning a new chapter toward the development of energy policy, technology and business ecosystem in all the countries. Distributed energy resources (DERs) are being largely interconnected to electrical power grids. This dispersed and intermittent generational mixes bring technical and economic challenges to the power systems in terms of operations, stability, reliability, interoperability and the policy making. In additional, DERs cause the significant impacts to the operation of traditional centralized generation power plants and the dispatch control centers. Under such circumstances, the accuracy of DERs power forecasting is one of the critical problems for TSO and DSO such as unit commitment, smooth fluctuations, peak load shifting, demand response, etc. In this paper, a simplified LSTM algorithm built over the architecture of Machine Learning methodology to forecast one day-ahead solar power generation is introduced. Through the machine learning processes of data processing, model fitting, cross validation, metrics evaluation and hyperparameters tuning, the result shows that the proposed simplified LSTM model outperform the MLP model. Moreover, the forecast of LSTM model can successfully capture intra-hour ramping on different weather scenarios. The average RMSE is 0.512 which is quite promising to inspire that the proposed methodology and architecture can best fit the short-term solar power forecasting applications.
53 citations
TL;DR: In this paper, the authors presented a developed security-constrained unit commitment (SCUC) problem for integrated power and NG networks in the presence of P2G, NG storage, and wind power resources.
35 citations
TL;DR: The concept of decision-dependent uncertainty (DDU) in the operational reliability evaluation is introduced and an adaptive reliability improvement UC (ARIUC) algorithm is proposed to efficiently solve the problem.
Abstract: 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.
31 citations
TL;DR: A novel cutting plane algorithm that makes use of the extremal distributions identified from the second-stage semidefinite programming (SDP) problems is introduced and 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 is shown.
Abstract: 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.
29 citations
TL;DR: In this paper, a generic data-driven framework for frequency-constrained unit commitment (FCUC) under high renewable penetration is proposed to address the challenge of frequency response and its security.
Abstract: With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.
28 citations
TL;DR: A rolling look-ahead unit commitment scheme in a combined PDN and DHN to exploit the operational flexibility of rapid-response combined heat and power (CHP) units under significantly variable renewable energy source (RES) power output is implemented.
Abstract: The combined operations of power distribution network (PDN) and district heating network (DHN) can enhance the flexibility and improve the overall energy efficiency of power systems. This article implements a rolling look-ahead unit commitment scheme in a combined PDN and DHN to exploit the operational flexibility of rapid-response combined heat and power (CHP) units under significantly variable renewable energy source (RES) power output. The scheme is formulated as a multistage distributionally robust (DR) unit commitment model that respects the non-anticipativity of decision variables for sequential revelations of uncertainties. In contrast to the moment-based ambiguity sets employed in conventional DR models, the proposed framework constructs an ambiguity set based on probabilistic forecasts. In this regard, a compatibility is achieved between DR approaches and probabilistic forecasts by incorporating comprehensive distribution information of RES power output stemming from probabilistic forecasts into DR models. The computational challenge associated with the proposed multistage DR model is addressed by applying linear decision rules. Moreover, a new constraint reformulation approach is utilized to increase the computational tractability. The proposed model will ultimately cast into a tractable mixed-integer linear programming problem. The effectiveness of the proposed method in capturing a comprehensive distribution of RES power output and reducing the the combined system operation cost is demonstrated by case studies carried out on the Barry Island multi-carrier energy system. Numerical results also validate the proposed model's improved computational performance.
28 citations
TL;DR: The studies show that machine learning, as model-free methods, is a valuable alternative or addition to the existing model-based methods and the effective combination of machine learning based approaches and physical model based methods are expected to derive more efficient UC solutions that can improve the secure and economic operation of power systems.
TL;DR: A distributed planning framework based on the alternating direction method of multipliers (ADMM), which uses the amount of electricity and natural gas required by EHs from each node as the decoupling information, and decomposes the joint planning problem into multiple planning sub-problems, respectively for the electric system, natural gas system and each EH.
TL;DR: 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, and both dynamic security and optimal exploitation of renewable and conventional units for power production and frequency support can be achieved.
Abstract: 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.
TL;DR: Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.
Abstract: This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.
TL;DR: A systematic performance comparison of 24 MILP models for designing multi-energy systems is conducted and models that consider part-load efficiencies lead to the lowest system costs but the highest computation times.
TL;DR: Simulations show that this novel hierarchical and robust scheduling paradigm for the emerging VSC-MTDC meshed AC/DC hybrid system with high share of wind power is effective in dealing with wind power uncertainty, promoting wind power accommodation, and improving the calculation efficiency.
Abstract: 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.
TL;DR: The proposed planning framework is used to assess the value of energy storage systems in the transition to a low-carbon power system and clearly outperforms the state of the art in terms of computational performance and accuracy.
TL;DR: In this paper, the authors presented a methodological approach to improve the planning capabilities by combining generation expansion and short-term planning models into one modelling system, namely TIMES-PL and MEDUSA, to design a pathway to carbon neutrality in the Polish power system.
TL;DR: A new capacity expansion planning method for wind power and ESs is proposed considering the actual multistage operation process of power system, and a more reasonable expansion planning decision is obtained and the computational efficiency is greatly improved by the proposed acceleration algorithm.
TL;DR: The integrated simulation and optimization results confirm the effectiveness and robustness of the proposed PBUC methodology.
TL;DR: Numerical results establish that the enforcement of DLR and the practice of OTS are complementary, which can help minimize the system operating cost, wind curtailment, and network congestion.
TL;DR: 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) is established and the operating model of nuclear power unit involved in peak load regulation is established to promote its operational flexibility.
TL;DR: A multi-objective unit commitment model which takes into account all of the above targets is established, and the proposed reliability measurement is able to realize a number of trade-offs between cost effective and solution robustness, thus providing decision support for system operators.
Abstract: Low cost, high reliability and low pollution are prime targets when performing current unit commitment optimization. As an extension of previous works, this study establishes a multi-objective unit commitment model which takes into account all of the above targets. The main content includes: First, the pricing support for thermal units with ultra-low emissions is involved when analyzing the operation cost of generation systems, which accords with the current policy of power markets. Second, a conditional Value-at-Risk-based measurement is formed to estimate system reliability considering the stochastic and fuzzy uncertainties existed in future load, renewable generation and equipment failures, which is sensitive to tail risks and provides easy-to-adjust conservativeness against worst-case scenarios. Third, to deal with the proposed model, a practical approach is applied to develop a multi-objective particle swarm optimization algorithm, which improves the Pareto fronts obtained by existing methods. The effectiveness of this research is exemplified by two case studies, which demonstrate that the model finds appropriate pricing support for the reformed units, and the proposed reliability measurement is able to realize a number of trade-offs between cost effective and solution robustness, thus providing decision support for system operators. Finally, the comparisons on performance metrics such as spacing and hyper-volume also justify the superiority of the algorithm.
TL;DR: This work introduces a radically new paradigm for addressing the optimal unit commitment problem, capable of accounting for the largely unaddressed challenge of the uncertain and volatile behavior of modern power systems, and guarantees global optimum solutions in non-convex optimization tasks in the least possible number of trials.
TL;DR: A decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems and the startup/shutdown variables of the thermal units and the variables in TG + ADN +-MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system.
TL;DR: This paper focuses on solving a robust network constrained unit commitment (NCUC) with TBES and DR programs, and an information gap decision theory (IGDT)-based robust optimization technique is proposed to obtain maximum robustness against the wind power uncertainty.
TL;DR: In this paper, the authors proposed a unit commitment model incorporating short-term voltage stability constraints (SVSCUC), which is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems.
Abstract: With the increasing development of AC/DC hybrid power systems, local synchronous generators in receiving-end grids are being replaced by high-voltage direct current (HVDC) power. The risk of short-term voltage instability exists because of the decline in dynamic reactive power resources from generators, the presence of high-proportioned induction motors and the demand for vast reactive power of HVDC under AC faults. In this situation, unit commitment (UC) should take short-term voltage stability into account. Hence, crucial generators can be retained to provide sufficient dynamic reactive power. This paper proposes a UC model incorporating short-term voltage stability constraints (SVSCUC). Furthermore, a decomposition-based solution approach is presented, which involves an alternate iterative solving of UC and differential-algebraic equations (DAEs). Time-domain simulation (TDS) is utilized to quantify the stability with an indicator. Linear stable cuts are formulated through trajectory sensitivity analysis and added into the UC. The UC is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems. The SVSCUC is illustrated using benchmark systems and a real system in eastern China. The results show the effectiveness and efficiency of the proposed methods. Violations of short-term voltage stability for credible contingencies are eliminated under the commitment of SVSCUC.
TL;DR: Comparison of the approaches shows that the DP algorithm with state prediction delivers a satisfying solution in significantly less time than DP and MILP, and the given linearity of the dependence of the computation time on number of steps is a superior advantage of the DP strategy.
TL;DR: A novel Mixed Integer Linear Programming (MILP) optimization algorithm has been developed to compute the optimal management of a micro-energy grid composed either by four Internal Combustion Generators (ICGs), or three ICGs and a Micro Gas Turbine (MGT).
TL;DR: A unit commitment considering combined electricity and reconfigurable heating network (UC-CERHN) is proposed in this paper to coordinate the day-ahead scheduling of power system and DHS.
Abstract: Massive adoptions of combined heat and power (CHP) units necessitate the coordinated operation of the power system and district heating system (DHS). Exploiting the reconfigurable property of district heating networks (DHNs) provides a potentially cost-effective solution to enhance the flexibility of the power system by readjusting the configuration for heat supply, which has not been addressed in the literature. To address this issue, a unit commitment considering combined electricity and reconfigurable heating network (UC-CERHN) is proposed in this paper to coordinate the day-ahead scheduling of power system and DHS. The DHS is formulated as a nonlinear and mixed-integer model considering the reconfigurable DHN. To make the commitment problem tractable, an auxiliary heat quantity variable is introduced, and the DHS model is reformatted to a linear energy flow model by approximation of heat loss, where the computational burdens are significantly reduced. Extensive case studies are presented to validate the effectiveness and accuracy of the approximated model and illustrate the potential benefits of the proposed method with respect to congestion management and wind power accommodation.