Optimal location-allocation of storage devices and renewable-based DG in distribution systems

Home-Ortiz, Juan M.; Pourakbari‐Kasmaei, Mahdi; Lehtonen, Matti; Mantovani, José Roberto Sanches

doi:10.1016/j.epsr.2019.02.013

Cited by 113 publications

(55 citation statements)

References 31 publications

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“…This portfolio management is appropriately addressed in the proposed technique by considering the factors expressed in (8). Correspondingly, a mixed integer conic programming model is employed in Home-Ortiz et al 30 to obtain the finest form, scope, and location of distributed generators (DGs) over a multistage planning horizon in the radial distribution systems. However, the uncertainty factor for the wind turbine system incorporation is not discussed comprehensively.…”

Section: Sensitivity Analysis and Discussionmentioning

confidence: 99%

“…29 The comprehensive modeling based on stochastic control for location, type, and size of new energy generation farms, storage units, and the distribution assets to be installed, reinforced, or replaced are proposed and discussed in the literature . [29][30][31][32] This paper proposes a model on the basis of annual system that contains impact of demand response and renewable resource intermittency in global cost function for optimal allocation of investment in new wind energy resources. This work has the following new contributions: (i) economic demand response model that interacts with the renewable energy market; (ii) separation of interhour demand response (medium and long term) from intrahour demand response (short term) impact; (iii) the study of a combined demand response/wind energy portfolio for large-scale electric grid system; and (iv) intermittent wind resources uncertainty cost formulation as well as jointly acting demand response.…”

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confidence: 99%

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Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

et al. 2019

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Summary Demand response is considered to be a realistic and comparatively inexpensive solution aimed at increasing the penetration of renewable generations into the bulk electricity systems. The work in this paper highlights the demand response in conjunction with the optimal capacity of installed wind energy resources allocation. Authors proposed a total annual system cost model to minimize the cost of allocating wind power generating assets. This model contains capacity expansion, production, uncertainty, wind variability, emissions, and elasticity in demand to find out cost per hour to deliver electricity. A large‐scale electric grid (25 GW) is used to apply this model. Authors discovered that demand response based on interhourly system is not as much helpful as demand response grounded on intrahourly system. According to results, 32% wind generation share will provide the least cost. It is also worth noting that optimal amount of wind generation is much sensitive to installation cost as well as carbon tax.

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Section: Sensitivity Analysis and Discussionmentioning

confidence: 99%

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confidence: 99%

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

et al. 2019

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“…In the proposed model, the behaviors of these uncertainties are considered through the historical data of electrical demand, wind speed, and solar irradiation that are divided into time blocks . The k-means clustering technique is applied to group the historical data to represent the load and the generation capacity of the RES [18].…”

Section: A Uncertainty Modelmentioning

confidence: 99%

“…In [17], a robust chance-constrained programming model was proposed to solve the DSEP problem with uncertainties in demand and RES, using a linearized model. Besides MILP models, convex programming models are among the most efficient approaches to be used for the optimal location of renewable-based DGs in the distribution network [18], [19]. In [18], a conic-based model was proposed to handle the optimal allocation and sizing of photovoltaic (PV) and wind, and dispatchable DG units with the inclusion of batteries.…”

Section: Introductionmentioning

confidence: 99%

“…Besides MILP models, convex programming models are among the most efficient approaches to be used for the optimal location of renewable-based DGs in the distribution network [18], [19]. In [18], a conic-based model was proposed to handle the optimal allocation and sizing of photovoltaic (PV) and wind, and dispatchable DG units with the inclusion of batteries. In [19], a conic programming model was D presented for the optimal simultaneous allocation of wind and gas turbines with the reinforcement of the existing network through the exchange of conductors gauges and the repowering of substations.…”

Section: Introductionmentioning

confidence: 99%

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A Mixed Integer Conic Model for Distribution Expansion Planning: Matheuristic Approach

Home-Ortiz

Pourakbari‐Kasmaei

Lehtonen

et al. 2020

IEEE Trans. Smart Grid

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This paper presents a mixed-integer conic programming model (MICP) and a hybrid solution approach based on classical and heuristic optimization techniques, namely matheuristic, to handle long-term distribution systems expansion planning (DSEP) problems. The model considers conventional planning actions as well as sizing and allocation of dispatchable/renewable distributed generation (DG) and energy storage devices (ESD). The existing uncertainties in the behavior of renewable sources and demands are characterized by grouping the historical data via the k-means. Since the resulting stochastic MICP is a convex-based formulation, finding the global solution of the problem using a commercial solver is guaranteed while the computational efficiency in simulating the planning problem of medium-or large-scale systems might not be satisfactory. To tackle this issue, the subproblems of the proposed mathematical model are solved iteratively via a specialized optimization technique based on variable neighborhood descent (VND) algorithm. To show the effectiveness of the proposed model and solution technique, the 24-node distribution system is profoundly analyzed, while the applicability of the model is tested on a 182node distribution system. The results reveal the essential requirement of developing specialized solution techniques for large-scale systems where classical optimization techniques are no longer an alternative to solve such planning problems.Index Terms-Distribution systems expansion planning, energy storage devices, VND-based matheuristic algorithm, mixed-integer conic programming, stochastic programming.

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Integrating PV+Battery Residential Microgrids in Distribution Networks: How Is the Point of Common Coupling Agreed Upon?

Saviuc

Passel

Peremans

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Optimal location-allocation of storage devices and renewable-based DG in distribution systems

Cited by 113 publications

References 31 publications

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

A Mixed Integer Conic Model for Distribution Expansion Planning: Matheuristic Approach

Integrating PV+Battery Residential Microgrids in Distribution Networks: How Is the Point of Common Coupling Agreed Upon?

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