Resiliency-oriented optimal scheduling of microgrids in the presence of demand response programs using a hybrid stochastic-robust optimization approach

With the development of electronic infrastructures and communication technologies and protocols, electric grids have evolved towards the concept of Smart Grids, which enable the communication of the different agents involved in their operation, thus notably increasing their efficiency. In this context, microgrids and nanogrids have emerged as invaluable frameworks for optimal integration of renewable sources, electric mobility, energy storage facilities and demand response programs. This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities and electric vehicle charging infrastructures. Moreover, a stochastic optimal scheduling tool is developed for the studied nanogrid, suitable for operators integrated into local service entities along with the energy retailer. A stochastic model is developed for fast charging stations in particular. A case study serves to validate the developed tool and analyze the economical and operational implications of demand response programs and charging infrastructures. Results evidence the importance of demand response initiatives in the economic profit of the retailer.

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Demand response models with correlated price data: A robust optimization approach

Applied Energy ◽

10.1016/j.apenergy.2012.01.016 ◽

2012 ◽

Vol 96 ◽

pp. 133-149 ◽

Cited By ~ 42

Author(s):

R.S. Ferreira ◽

L.A. Barroso ◽

M.M. Carvalho

Keyword(s):

Robust Optimization ◽

Demand Response ◽

Optimization Approach ◽

Response Models ◽

Price Data

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A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing

Energies ◽

10.3390/en11123258 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3258 ◽

Cited By ~ 8

Author(s):

Feihu Hu ◽

Xuan Feng ◽

Hui Cao

Keyword(s):

Robust Optimization ◽

Demand Response ◽

Electricity Market ◽

Decision Model ◽

Expected Profit ◽

Optimization Approach ◽

Short Term ◽

Spot Prices ◽

Retail Prices ◽

Time Of Use Pricing

This paper establishes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and electricity retail prices. The electricity procurement process includes purchasing electricity from generation companies and from the spot market. The selling prices of electricity for the customers are based on time-of-use (TOU) pricing which is widely employed in modern electricity market as a demand response program. The objective of the model is to maximize the expected profit of the retailer through optimizing the electricity procurement strategy and electricity pricing scheme. A price elasticity matrix (PEM) is adopted to model the demand response. Also, uncertainty in spot prices is modeled using a robust optimization approach, in which price bounds are considered instead of predicted values. Using a robust optimization approach, the retailer can adjust the level of robustness of its decisions through a robust control parameter. A case study is presented to illustrate the performance of the model. The simulation results demonstrate that the developed model is effective in increasing the expected profit of the retailer and flattening the load profiles of customers.

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Application of Robust Optimization Approach to Determine Optimal Retail Electricity Price in Presence of Intermittent and Conventional Distributed Generation Considering Demand Response

Journal of Control Automation and Electrical Systems ◽

10.1007/s40313-017-0328-9 ◽

2017 ◽

Vol 28 (5) ◽

pp. 664-678 ◽

Cited By ~ 7

Author(s):

Hessam Golmohamadi ◽

Reza Keypour

Keyword(s):

Robust Optimization ◽

Distributed Generation ◽

Demand Response ◽

Electricity Price ◽

Optimization Approach

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The role of correlated prices in demand response: A robust optimization approach

2011 IEEE PES CONFERENCE ON INNOVATIVE SMART GRID TECHNOLOGIES LATIN AMERICA (ISGT LA) ◽

10.1109/isgt-la.2011.6083191 ◽

2011 ◽

Author(s):

R. S. Ferreira ◽

L. A. Barroso ◽

M. M. Carvalho ◽

R. Kelman ◽

P. R. Lino ◽

...

Keyword(s):

Robust Optimization ◽

Demand Response ◽

Optimization Approach

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An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities

Buildings ◽

10.3390/buildings11060237 ◽

2021 ◽

Vol 11 (6) ◽

pp. 237

Author(s):

Seyyed Danial Nazemi ◽

Mohsen A. Jafari ◽

Esmat Zaidan

Keyword(s):

Nonlinear Optimization ◽

Demand Response ◽

Commercial Buildings ◽

Mixed Integer ◽

Programming Approach ◽

Optimization Approach ◽

Peak Demand ◽

Load Scheduling ◽

Smart Building ◽

Demand Response Programs

The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings.

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