1,720,982 research outputs found
Multi-energy islands: Advances in local district heating, cooling and power systems
No full textThe trend of the global energy matrix change is toward a smart energy system with an optimized interaction between the demand-side and the supply-side. In this perspective, a smart energy system has many features such as a high share of renewable energy systems, concrete integration of energy systems, active interactions of different energy sectors, utilization of the most advanced clean energy technologies, the lowest rate of losses using local energy systems (i.e., the so-called energy islands) and the highest possible rate of utilization of waste or freely available energy flows. On the other hand, fast-growing of energy production from renewable energy sources offers more attractive, cost-effective opportunities to design and employ the local energy systems. Although integrating more renewable energy sources is a technological and economic challenge for the electricity network, it can be accounted as an opportunity to optimize the electricity system operation in synergy with other energy vectors such as district heating/cooling or gas networks to increase the hosting capacity for renewable sources
Risk-involved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model
No full textIn this paper, a hybrid power generation company consisting of a concentrated solar power unit, wind turbines, a battery system, and a demand response provider is established to take part in electricity markets. The operating strategy of the hybrid power generation company in day-ahead and adjustment (intraday) markets is determined based on their coordinated operation. To tackle the intrinsic uncertainties, for the first time, a mixed stochastic-interval model is proposed which addresses the uncertainty in demand response and solar energy via interval optimization. The examined problem is formulated as a multi-objective optimization problem in which the risk of both stochastic and interval parameters can be involved. On this basis, the proposed operating strategy covers three objective functions, namely, expected radius and midpoint of the hybrid power generation company's profit together with the conditional value-at-risk. Accordingly, the normal boundary intersection and lexicographic optimization techniques are utilized to derive feasible solutions. Lastly, numerical results are presented and the performance of the proposed framework is investigated. The results indicate that the suggested model can be efficiently used to handle the decision-maker's preference over interval and stochastic parameters, and the risk criterion associated with interval parameters becomes larger as the forecasting errors increase
Short-term reliability and economic evaluation of resilient microgrids under incentive-based demand response programs
No full textIn this paper, a flexibility oriented stochastic scheduling framework is presented to evaluate short-term reliability and economic of islanded microgrids (MGs) under different incentive-based DR (IBDR) programs. A multi-period islanding constraint is considered to prepare the MG for a resilient response once a disturbance occurs in the main grid. Also, a multi-segment optimal power flow (OPF) approach is used to model the IBDR actions and reserve resources. Moreover, uncertainties associated with electricity prices, loads, renewable generation, calls for reserve as well as uncertainties of islanding duration of the MG are considered. The ultimate goal of the MG operator is to maximize its expected profit under a certain level of security and reliability in conjunction with the minimization of energy procurement costs of customers. The MG's economy and reliability indices are studied considering normal operation and resilient condition based on appliances characteristics, customers’ and operator's behaviors. The proposed model can effectively manage MGs operation in both normal and resilient conditions in order to improve economic and reliability indices. Numerical results demonstrate that by implementing IBDR, in cases of normal and resilient operation, the expected profit of the MG operator increases about 4% and 2.7% and reliability indicator improved 60% and 56%, respectively
Offering and bidding for a wind producer paired with battery and CAES units considering battery degradation
No full textThis paper presents a stochastic framework for offering and bidding strategies of a hybrid power generation system (HPGS) with a wind farm and two types of energy storage facilities, i.e., compressed air energy storage (CAES) and battery energy storage (BES) systems. The model considers the participation of the HPGS in consecutive electricity markets, i.e., day-ahead (DA) and intraday markets. To better address the proposed trading strategy problem, the BES degradation cost is also incorporated into the model. Furthermore, a mechanism based on energy procurement from demand response resources (DRRs) in the intraday demand response exchange (IDREX) market for the HPGS is also established to offset unexpected energy imbalances effectively. The suggested offering and bidding strategy is formulated as a three-stage stochastic programming problem incorporating a risk-alleviating index, namely, the conditional value-at-risk (CVaR). Results from several simulations indicate considerable profit gain and risk reduction achieved by the suggested offering and bidding framework
Flexible stochastic scheduling of microgrids with islanding operations complemented by optimal offering strategies
No full textThis paper presents a stochastic framework for optimal scheduling of microgrids (MGs) considering unscheduled islanding events, initiated by disturbances in the main grid. This scheduling approach considers different uncertainties and determines the day-ahead schedule of the resources considering emergency operations. The proposed strategy attempts to effectively manage demand and supply side resources to mitigate the effects of uncertainties in both normal and emergency operations. The prevailing uncertainties associated with renewable power generations, demand and electricity prices as well as uncertainties of islanding duration are addressed in the presented framework. The objective is to maximize the expected profit of the operator over the scheduling horizon, while restricting the risk of mandatory load shedding imposed by uncertain parameters within an acceptable level. According to the proposed strategy, an efficient probabilistic index is obtained from generation reserve margin (GRM) in islanded mode, and applied to create a proper offering price signal to coordinate responsive loads with renewable generations providing more reliable operations. The effectiveness of the proposed strategy in terms of economy and reliability is investigated via a comparison with other methods. Extensive numerical results illustrate that the proposed offering price strategy can improve the MG's operation from both reliability and economic aspects
Risk-averse probabilistic framework for scheduling of virtual power plants considering demand response and uncertainties
No full textIn this paper, a risk-based stochastic framework is presented for short-term energy and reserve scheduling of a virtual power plant (VPP) considering demand response (DR) participation. The VPP comprises several dispatchable generation units, battery energy storage systems (BESSs), wind power units, and flexible loads. The proposed scheduling framework is formulated as a risk-constrained stochastic program to maximize the VPP's profit considering uncertainties of loads, wind energy and electricity prices as well as N-1 contingencies. The proposed model considers both supply and demand-sides capability for providing and deploying reserves in order to optimize the use of resources while satisfying N-1 security and other constraints. Moreover, the effect of risk-aversion on decision making of the VPP in the offering/bidding power and required reserve services is investigated by implementing conditional value-at-risk (CVaR) in the optimization model. The proposed scheme is implemented on a test VPP and the energy and reserve scheduling with and without DR participants is addressed in detail through a numerical study. Moreover, the effects of the operator's risk-averse behavior on the VPP energy and reserve management and its security indices are investigated
Optimal Risk-Constrained Stochastic Scheduling of Microgrids with Hydrogen Ve-hicles in Real-time and Day-ahead Markets
No full textBy growing interest in hydrogen vehicles (HVs), hydrogen fueling stations (HFSs) that convert electric power to hydrogen to supply HVs have emerged as a new asset for power grids. To safely and consistently supply HFSs with power, the use of microgrids (MGs), including various flexible generation units, is considered to be a reliable choice. This paper proposes a competence MG scheduling model. In this model, an optimal coordination of HFSs with demand response (DR), energy storage systems (ESS), and appropriate multi-market mechanisms is addressed. Also, a reformulated version of a risk-constrained stochastic scheduling (RSS) model is used to minimize the MG operation cost. The uncertainties associated with the real-time market price, renewables, electrical loads, and HVs are handled by considering conservativeness parameters. In this model, linearized AC optimal power flow (ACOPF) equations are included in the mixed-integer linear programming (MILP) problem to satisfy the security of MG operation. The proposed model is examined on a 21-bus MG while considering various case studies. The results show that the operation of HFSs provides a profit of 254.5$/day by selling hydrogen for MG operator. In addition, it is found that developing the HFS technology can reduce the total daily operation costs of MG by up to 9.6%. It is also shown that participation of MG in both day-ahead and real-time markets leads to a reduction of 11.7% in the operating costs. Moreover, we show that employing DR programs leads to operation cost reduction and load flattening during high-demand hours. The security constraints keep the voltage between 0.97 p.u. and 1 p.u and the loss of lines within a reasonable range. Finally, a comprehensive comparison between our perceptive RSS with traditional stochastic scheduling and conservative RSS is carried out to show the effectiveness of the proposed method.<br/
Offering strategy of thermal-photovoltaic-storage based generation company in day-ahead market
No full textDesigning appropriate strategies for the participation of generation companies (GenCos) in the electricity markets has always been a concern for researchers. Generally, a set of dispatchable and non-dispatchable units constitute GenCos. This chapter presents a coordinated offering structure for the participation of a GenCo consisting of thermal, photovoltaic (PV), and battery storage system (BSS) in the day-ahead (DA) electricity market. The proposed offering structure is formulated as a three-stage stochastic programming problem while a scenario-based technique is utilized to handle the uncertainty related to electricity prices and PV production. From another point of view, a compatible risk-measuring index with multi-stage stochastic programming problems, namely conditional value at risk (CVaR), is also considered in the proposed structure. The proposed offering model is not only able to derive the offering curves of GenCo but also is capable of applying various emission limitations pertaining to thermal units
Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model
No full textRenewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In the current paper, a three-stage stochastic multi-objective offering framework based on mixed-integer programming formulation for a wind-thermal-energy storage generation company in the energy and spinning reserve markets is proposed. The commitment decisions of dispatchable energy sources, the offering curves of the generation company in the energy and spinning reserve markets, and dealing with energy deviations in the balancing market are the decisions of the proposed three-stage offering strategy problem, respectively. In the suggested methodology, the participation model of the energy storage system in the spinning reserve market extends to both charging and discharging modes. The proposed framework concurrently maximizes generation company's expected profit and minimizes the expected emission of thermal units applying lexicographic optimization and hybrid augmented-weighted ∊-constraint method. In this regard, the uncertainties associated with imbalance prices and wind power output as well as day-ahead energy and spinning reserve market prices are modeled via a set of scenarios. Eventually, two different strategies, i.e., a preference-based approach and emission trading pattern, are utilized to select the most favored solution among Pareto optimal solutions. Numerical results reveal that taking advantage of spinning reserve market alongside with energy market will substantially increase the profitability of the generation company. Also, the results disclose that spinning reserve market is more lucrative than the energy market for the energy storage system in the offering strategy structure
Artificial Intelligence-Based Prediction and Analysis of the Oversupply of Wind and Solar Energy in Power Systems
No full textThe economic viability of renewable energy is deteriorating due to its curtailment in power systems. Therefore, it is imperative to forecast curtailments for more effective utilization. To alleviate this issue, in this paper, we propose artificial intelligent-based models to accurately predict wind and solar power curtailments (WSPCs), which have not been investigated before. In this regard, a prediction methodology is developed using different types of machine learning (ML) methods and evaluated based on both hold-out (HO) and cross-validation (CV) approaches. The ML methods considered include regression trees (RT), gradient boosting trees (GBT), random forest (RF), feed-forward artificial neural networks (ANN), long short-term memory (LSTM), and support vector machines (SVR). The prediction models are trained based on eight input features, including load demands, the output power of thermal power plants, nuclear units, solar farms, wind turbines, biomass/geothermal units, large hydro units, power imports, and WSPC as two target variables. Based on historical data, i.e., hourly records of California independent system operator (ISO), the predictive models are validated, and the optimal hyperparameters are chosen using Bayesian optimization for each model to attain the best results. Among all the models, the RF model results in the minimum prediction errors and thus the best performance by implementing the proposed CV approach. The obtained results demonstrate the effectiveness of the proposed models in the prediction of WSPCs
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