Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy

Lu, Runzhao; Ding, Tao; Qin, Boyu; Jin, Ma; Fang, Xiuzhong; Dong, Zhaoyang

doi:10.1109/tste.2019.2918269

Cited by 138 publications

(52 citation statements)

References 45 publications

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“…Q bg (t) = P bg (t)η bg (34) where (31) represents the minimum P bg min and maximum P bg max operating limits of the biofuel generator; the ramping constraint is given by (32), where P bg ramp is the maximum allowable ramp-rate; the minimum up-time and down-time limits, t up min and t down min , respectively, are imposed by (33). Equation (34) gives the thermal power generation from biofuel generator, where Q bg and η bg are the thermal output and thermal efficiency of the biofuel generator, respectively.…”

Section: Multi-energy Management System (Mems)mentioning

confidence: 99%

“…A two-stage scenario-based stochastic algorithm is proposed for handling heterogeneous uncertainties for scheduling of multi-energy micro-grids [30,31]. Lu et al [32] have handled the uncertainty of RE for economic dispatch of the IEEE 118-bus system by a multi-stage stochastic programming model. Optimal and reliable multi-energy supply under the uncertainties having thermal storage, diesel generators and renewable energy are handled by a robustly coordinated operation approach [33] for minimising the operating costs.…”

Section: Introductionmentioning

confidence: 99%

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Optimal sizing and multi‐energy management strategy for PV‐biofuel‐based off‐grid systems

2020

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This study proposes a comprehensive framework for developing a multi-energy off-grid microgrid with the decoupled flow of thermal and electrical energies in a rural setting. A carbon-neutral microgrid with a hybrid generation system constituting a photovoltaic unit and a biofuel generator is proposed. In order to enhance the fuel utilisation efficiency, the biofuel generator is operated in combined cooling, heating, and power mode, and the recovered thermal energy forms the heat distribution network in the microgrid. The flexibility of system operation is improved by suitable multi-energy (electrical and thermal) storage. Firstly, an optimal sizing framework has been developed for the system as a mixed integer linear programming model. Secondly, a coordinated multi-energy management system (MEMS) has been developed for combined optimal dispatch of multiple generation and storage resources. The MEMS has been developed as a mixed integer non-linear programming model, which minimises system operational cost while considering minimum battery degradation to prolong its lifetime. Finally, a detailed economic analysis of the proposed system has been presented, highlighting the levellised cost of energy and net present value. Extensive case studies and simulation results depict the effectiveness and suitability of the proposed MEMS for the rural off-grid microgrid.

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Section: Multi-energy Management System (Mems)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal sizing and multi‐energy management strategy for PV‐biofuel‐based off‐grid systems

2020

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“…In this paper, assume the closed-form expression of the logarithmic moment generating function is not available, thus (36) is a typical stochastic optimization problem with a fixed probability distribution P 0 [2]. Standard stochastic optimization technique can be used to solve (36) such as the sample average approximation [13] and stochastic approximation. To apply the SAA method, we generate an independent and identical distribution sample from the distribution P 0 , and we use the following optimization problem to approximate problem (36), which can be written as the following form:…”

Section: Solution Strategymentioning

confidence: 99%

“…Stochastic approximation [6] and sample average approximation (SAA) [7] are two typical solution methods for stochastic programming. In these studies [8][9][10][11][12][13], results demonstrate that stochastic optimization has a good performance under uncertainties. Generally, in stochastic optimization, the variabilities are assumed to follow a determined probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

et al. 2019

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This paper proposes a distance-based distributionally robust energy and reserve (DB-DRER) dispatch model via Kullback-Leibler (KL) divergence, considering the volatile of renewable energy generation. Firstly, a two-stage optimization model is formulated to minimize the expected total cost of energy and reserve (ER) dispatch. Then, KL divergence is adopted to establish the ambiguity set. Distinguished from conventional robust optimization methodology, the volatile output of renewable power generation is assumed to follow the unknown probability distribution that is restricted in the ambiguity set. DB-DRER aims at minimizing the expected total cost in the worst-case probability distributions of renewables. Combining with the designed empirical distribution function, the proposed DB-DRER model can be reformulated into a mixed integer nonlinear programming (MINLP) problem. Furthermore, using the generalized Benders decomposition, a decomposition method is proposed and sample average approximation (SAA) method is applied to solve this problem. Finally, simulation result of the proposed method is compared with those of stochastic optimization and conventional robust optimization methods on the 6-bus system and IEEE 118-bus system, which demonstrates the effectiveness and advantages of the method proposed.

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“…In [11], the authors propose a stochastic-robust approach for the decisions of a system with EVs. In [12] a stochastic MILP formulation is proposed to assess the impact of RES and EV uncertainty on the energy management of a smart building, while in [13] and [14] the authors used stochastic dual dynamic programming to address RES uncertainty in dispatch problems. However, with multiple DERs, system parameters span over an exponentially large space, which means that the relatively very small number of scenarios sampled by a stochastic programming method can fail to generalize reliably.…”

Section: Introductionmentioning

confidence: 99%

Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

Τσαούσογλου¹,

Mitropoulou²,

Steriotis³

et al. 2021

Preprint

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<div>In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty.</div><div>A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.</div>

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Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy

Cited by 138 publications

References 45 publications

Optimal sizing and multi‐energy management strategy for PV‐biofuel‐based off‐grid systems

Optimal sizing and multi‐energy management strategy for PV‐biofuel‐based off‐grid systems

Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

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