A Smart Voltage Optimization Approach for Industrial Load Demand Response

2019 ◽  
Author(s):  
Adarsh Madhavan ◽  
Brian Lee ◽  
Claudio A. Canizares ◽  
Kankar Bhattacharya
Keyword(s):  
Demand Response ◽  
Optimization Approach ◽  
Load Demand

scholarly journals A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 425 ◽  
Author(s):  
Yao Wang ◽  
Yan Lu ◽  
Liwei Ju ◽  
Ting Wang ◽  
Qingkun Tan ◽  
...  
Keyword(s):  
Demand Response ◽  
Gas Turbines ◽  
Energy System ◽  
Objective Functions ◽  
Distributed Energy ◽  
Operating Income ◽  
Hybrid Energy ◽  
Multi Objective ◽  
Load Demand ◽  
Load Fluctuation

In order to meet the user’s electricity demand and make full use of distributed energy, a hybrid energy system (HES) was proposed and designed, including wind turbines (WTs), photovoltaic (PV) power generation, conventional gas turbines (CGTs), incentive-based demand response (IBDR), combined heat and power (CHP) and regenerative electric (RE) boilers. Then, the collaborative operation problem of HES is discussed. First, the paper describes the HES’ basic structure and presents the output model of power sources and heating sources. Next, the maximum operating income and minimum load fluctuation are taken as the objective function, and a multi-objective model of HES scheduling is proposed. Then an algorithm for solving the model is proposed that comprises two steps: processing the objective functions and constraints into linear equations and determining the optimal weight of the objective functions. The selected simulation system is a microgrid located on an eastern island of China to comparatively analyze the influence of RE-heating storage (RE-HS) and price-based demand response (PBDR) on HES operation in relation to four cases. By analyzing the results, the following three conclusions are drawn: (1) HES can comprehensively utilize a variety of distributed energy sources to meet load demand. In particular, RE technology can convert the abandoned energy of WT and PV into heat during the valley load time, to meet the load demand combined with CHP; (2) The proposed multi-objective scheduling model of HES operation not only considers the maximum operating income but also considers the minimum load fluctuation, thus achieving the optimal balancing operation; (3) RE-HS and PBDR have a synergistic optimization effect, and when RE-HS and PBDR are both applied, an HES can achieve optimal operation results. Overall, the proposed decision method is highly effective and applicable, and decision makers could utilize this method to design an optimal HES operation strategy according to their own actual conditions.

Optimal energy mix for a reverse osmosis desalination unit considering demand response

2020 ◽  
Vol 18 (5) ◽  
pp. 1287-1303 ◽  
Author(s):  
Ewaoche John Okampo ◽  
Nnamdi I. Nwulu
Keyword(s):  
Reverse Osmosis ◽  
Demand Response ◽  
Unit Cost ◽  
Energy Sources ◽  
Optimization Approach ◽  
Diesel Generator ◽  
Content Type ◽  
Energy Mix ◽  
Optimal Sizing ◽  
Optimal Energy

Purpose Reverse osmosis (RO) has become an important method of desalination to meet the ever-growing water needs around the world. Its integration with renewable energy source (RES) reduces the environmental impact of gas emissions and cost of conventional fossil energy sources. The optimal sizing of energy sources to power RO desalination system is intended mainly to minimize the annualized cost of the system and by extension minimize freshwater cost while maximizing production. Design/methodology/approach In this study, a mathematical optimization approach is used to determine the optimal energy mix, which includes grid power, diesel generator and a photovoltaic (PV) module to supply an RO desalination unit. Three cases of optimal sizing approach were compared. Case 1 is a system with only grid power and diesel generator as energy sources; Case 2 has PV incorporated in the energy supply mix while Case 3 has the three energy sources and a Time of Use (TOU) demand response program on the demand side. Findings The results of implementing the optimization models show that Case 3 turnout the highest freshwater production (1,521 m3/day) at a unit cost of 1.36$/m3 when compared to Case 1 with daily freshwater production of 1,250 m3/day at a unit cost of 1.68$/m3 and Case 2 having a daily freshwater production of 1,501 m3/day at a unit cost of 1.33$/m3. Originality/value The integration of RES to power desalination system with application of TOU demand response is the significance of this study.

scholarly journals A Multi-Objective Optimization Approach towards a Proposed Smart Apartment with Demand-Response in Japan

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 127 ◽  
Author(s):  
Yuta Susowake ◽  
Hasan Masrur ◽  
Tetsuya Yabiku ◽  
Tomonobu Senjyu ◽  
Abdul Motin Howlader ◽  
...  
Keyword(s):  
High Voltage ◽  
Demand Response ◽  
Low Voltage ◽  
Heat Pumps ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective ◽  
Trade Offs ◽  
To Receive

In Japan, residents of apartments are generally contracted to receive low voltage electricity from electric utilities. In recent years, there has been an increasing number of high voltage batch power receiving contracts for condominiums. In this research, a high voltage batch receiving contractor introduces a demand–response in a low voltage power receiving contract, which maximizes the profit of a high voltage batch receiving contractor and minimizes the electricity charge of residents by utilizing battery storage, electric vehicles (EV), and heat pumps. A multi-objective optimization algorithm calculates a Pareto solution for the relationship between two objective trade-offs in the MATLAB ® environment.

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

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3258 ◽  
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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