Optimal scheduling of distributed energy resources and responsive loads in islanded microgrids considering voltage and frequency security constraints

Int Trans Electr Energ Syst

et al. 2018

Self Cite

Summary This paper presents a risk‐averse stochastic bi‐level programming approach to solve decision‐making of a retailer in a competitive market under uncertainties. The retailer decides the level of involvement in day‐ahead (DA) and regulation markets by making an optimal bidding strategy with the goal of expected profit maximization. Uncertainties associated with DA prices, up/down regulation market prices, customers' demand, and rival retailers' behaviors are tackled through a stochastic programming model. In the proposed model, responsive loads and electric vehicles (EVs) track the real‐time prices and choose the proper retailer to minimize their payments in the competitive trading floor. The obtained nonlinear stochastic model is transformed into an equivalent linear single‐level program by replacing the lower‐level problem with its Karush‐Kuhn‐Tucker optimality conditions and using duality theory. Finally, the proposed methodology is evaluated by applying to a realistic case study, and the results demonstrate the effectiveness of the proposed framework.

“…Therefore, the energy consumption changes from

E_{t}^{int}

to E t in period t as below:

E_{t} = E_{t}^{int} + normalΔ E_{t} .

The benefit of customers is obtained as below:

S ((), E_{t}) = B ((), E_{t}) - E_{t} . ρ_{t}^{D},

\frac{\partial S ((), E_{t})}{\partial E_{t}} = \frac{\partial B ((), E_{t})}{\partial E_{t}} - ρ_{t}^{D} = 0 .

Based on the model represented in Vahedipour‐Dahraie et al, the energy consumption of customers at time t is obtained as follows:

E_{t} = E_{t}^{int} . exp \sum_{h \in T} {Elas}_{t, t} . ln ([], \frac{ρ_{t}^{D}}{ρ_{t}^{D, int}} + \frac{1}{1 + {Elas}_{t, t}^{- 1}}) .

…”

Section: Risk‐constrained Stochastic Bi‐level Decision‐making Problemmentioning

confidence: 99%

Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Int Trans Electr Energ Syst

et al. 2018

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“…Electricity network operator deals with a feasible operation with minimum cost. To simplify the model, DC load flow is used to be sure about technical constraints [17]. The objective function of this optimization problem comprises of two terms.…”

Section: B Electricity Operation Modelmentioning

confidence: 99%

Integrated Expansion Planning of Gas-Electricity System: A Case Study in Iran

Khaligh

Buygi

2018 International Conference on Smart Energy Systems and Technologies (SEST)

et al. 2018

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Gas-fired power plants are connection points between gas and electricity networks which are growing in installation due to their high efficiency rates and flexibilities. The main purpose of this paper is to adjust integrated expansion planning of gas-electricity system. In integrated expansion planning it is assumed that a central entity such as Ministry of Energy is responsible for the expansion of both gas and electricity networks. Results of the proposed method are examined in Khorasan province of Iran as a realistic case study which has a high penetration level of gas-driven units. To demonstrate the effectiveness of the proposed method, results are compared with those of an independent expansion planning method. Also sensitivity to load level growth forecast is analyzed.

“…There is an appearing consensus that demand‐side management (DSM) can have an active duty in keeping balance between supply and demand in future smart grids . At demand side of a smart restructured power system, responsive loads can not only supply various types of demand response (DR) services such as peak load shifting and ancillary services, but also contribute heavily in reduction of operating cost and emission as well as improvement of system reliability .…”

Section: Introductionmentioning

confidence: 99%

A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Int Trans Electr Energ Syst

2019

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Summary This paper investigates a stochastic bi‐level scheduling model for decision‐making of a load‐serving entity (LSE) in competitive day‐ahead (DA) and regulating markets with uncertainties. In this model, LSE as the main interacting player of the market sells electricity to end‐use customers and plug‐in electric vehicles (PEVs) to maximize its expected profit. Therefore, a two‐level decision‐making process with different objectives is considered to solve the problem. In one level, the objective is to maximize the LSE's profit by optimally scheduling of responsive loads and PEVs charging/discharging process, while in the other level, the payments of the customers and PEV owners should be minimized in a competitive market. In the proposed decision‐making process, to model the uncertainties, market prices, required energy of customers and PEVs, and the rival LSEs' prices are considered as random variables. The bi‐level stochastic problem is then converted into a linear single‐level stochastic model with equilibrium constraints by using Karush‐Kuhn‐Tucker (KKT) optimality conditions as well as duality theory. A case study is implemented to indicate the applicability of the intended model. The applicability of the proposed model is tested on Nordic market and the results show that in a competitive market, the LSE can increase its revenue and attract more demand loads and PEV owners by offering more moderate prices.