In electrical power systems, the natural load randomness requires modeling the uncertainty adequately for determining optimal operation decisions. Besides security system actions and reserve management, stochastic app...
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In electrical power systems, the natural load randomness requires modeling the uncertainty adequately for determining optimal operation decisions. Besides security system actions and reserve management, stochastic approaches to solve operation problems have been widely considered as an approximation for mitigating demand fluctuations and renewable energy variability. This study proposes a scenario-based stochastic Preventive Security-Constrained Economic Dispatch formulation using power transfer distribution factors to model the transmission network considering Nk line outages and transmission losses. Extensive computational simulations have conducted with different electrical power systems to demonstrate improvements in the power system operation obtained by the proposed stochastic formulation.
Charging station availability is crucial for a thriving electric vehicle market. Due to budget constraints, locating these stations usually proceeds in phases, which calls for careful consideration of the (random) cha...
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As renewable energy rapidly develops, microgrids have gradually become an essential part of modern power systems. Microgrids integrate distributed generation, energy storage systems, and loads, improving energy utiliz...
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The advancements in Internet-connected drones and edge computing raise the possibility of an on-the-fly inspection service that can instantly report the results. Inspection of sites using a drone fleet requires planni...
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The advancements in Internet-connected drones and edge computing raise the possibility of an on-the-fly inspection service that can instantly report the results. Inspection of sites using a drone fleet requires planning to meet situational requirements while minimizing operational costs under uncertainties of inspection requests, the urgency of reports, and the availability of communication channels. In this work, with the restriction on drone flying time, we decompose the planning into two phases: 1) precomputing groups of sites and 2) three-stage stochastic programming. The former phase generates feasible groups of sites, each of which is served by a drone, and precomputes the minimum-cost flying path for each group. The latter phase, given the feasible groups, jointly optimizes drone placement and data delivery under the uncertainties. The two-phase approach allows the planning to scale up to a practical situation. The performance evaluations show that the overall cost can be saved even if uncertainties exist, and the proposed approach significantly outperforms other methods, which do not consider the uncertainties. For a larger problem size, a heuristic algorithm is proposed to trade a loss in the optimality of 1.05-1.09 times the cost with 3.67-483.97 times speed-up in computation time.
The paper investigates analytical properties of dynamic probabilistic constraints (chance constraints). The underlying random distribution is supposed to be continuous. In the first part, a general multistage model wi...
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The paper investigates analytical properties of dynamic probabilistic constraints (chance constraints). The underlying random distribution is supposed to be continuous. In the first part, a general multistage model with decision rules depending on past observations of the random process is analyzed. Basic properties like (weak sequential) (semi-) continuity of the probability function or existence of solutions are studied. It turns out that the results differ significantly according to whether decision rules are embedded into Lebesgue or Sobolev spaces. In the second part, the simplest meaningful two-stage model with decision rules from L-2 is investigated. More specific properties like Lipschitz continuity and differentiability of the probability function are considered. Explicitly verifiable conditions for these properties are provided along with explicit gradient formulae in the Gaussian case. The application of such formulae in the context of necessary optimality conditions is discussed and a concrete identification of solutions presented.
New energy integration and flexible demand response make smart grid operation scenarios complex and change-able,which bring challenges to network *** every possible scenario is considered,the solution to the plan-ning...
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New energy integration and flexible demand response make smart grid operation scenarios complex and change-able,which bring challenges to network *** every possible scenario is considered,the solution to the plan-ning can become extremely time-consuming and *** paper introduces statistical machine learning(SML)techniques to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution *** proposed SML includes linear regression,probability distribu-tion,Markov chain,isoprobabilistic transformation,maximum likelihood estimator,stochastic response surface and center point *** on the above SML model,capricious weather,photovoltaic power generation,thermal load,power flow and uncertainty programming are *** a 33-bus distribution system as an example,this paper compares the stochastic planning model based on SML with the traditional models published in the *** results verify that the proposed model greatly improves planning performance while meeting accuracy *** case study also considers a realistic power distribution system operating under stressed conditions.
A significant quantity of electric vehicles and distributed generator (DG) sources are connected to the distribution network (DN), resulting in the formation of a new power-transportation network (PTN). This integrati...
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This paper determines the location of emergency supply points by constructing a multi-stage stochastic programming model with the goal of minimizing rescue costs. and reserves, and considers the robust optimization mo...
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With the rapid development of the digital economy and the continuous dynamic changes in demand for cloud services, a single cloud resource service provider is no longer capable of meeting the diverse requirements of t...
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Battery energy storage (BES) and demand response (DR) are two important resources to increase the operational flexibility of a virtual power plant (VPP) and thus reduce the economic risks that VPP faces in the short-t...
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Battery energy storage (BES) and demand response (DR) are two important resources to increase the operational flexibility of a virtual power plant (VPP) and thus reduce the economic risks that VPP faces in the short-term electricity market. This article develops a data-driven approach for VPP resource planning (VRP), in which BES sizing and DR customer selection are optimized synergistically to maximize VPP's profit in the electricity market. Heterogeneity in DR potential across individual customers is considered in the planning framework by utilizing the knowledge learnt from smart meter data. The overall VRP problem is formulated by a risk-managed, multistage stochastic programming framework to address the uncertainties from the intermittent renewable energy sources, load demands, market prices, and DR resources. Case studies compare the VRP results under two market imbalance settlement settings, namely, penalty-charged and penalty-free markets. The results demonstrate that jointly optimizing BES and DR customer selection leveraging the smart meter data can improve the VPP's expected profit under both market settings.
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