Demand Response of an Industrial Buyer considering Congestion and LMP in Day-Ahead Electricity Market

2019 ◽  
Vol 20 (3) ◽  
Author(s):  
Arvind Kumar Jain
Keyword(s):  
New York ◽  
New England ◽  
Electricity Markets ◽  
Electricity Market ◽  
Linear Optimization ◽  
High Price ◽  
Bidding Strategy ◽  
Purchase Cost ◽  
Stochastic Linear Optimization ◽  
Bus System

Abstract This paper proposes a methodology for developing Price Responsive Demand Shifting (PRDS) based bidding strategy of an industrial buyer, who can reschedule its production plan, considering power system network constraints. Locational Marginal Price (LMP) methodology, which is being used in PJM, California, New York, and New England electricity markets, has been utilized to manage the congestion. In this work, a stochastic linear optimization formulation comprising of two sub-problems has been proposed to obtain the optimal bidding strategy of an industrial buyer considering PRDS bidding. The first sub-problem is formulated as to maximize the social welfare of market participants subject to operational constraints and security constraints to facilitate market clearing process, while the second sub-problem represents the industrial buyer’s purchase cost saving maximization. The PRDS based bidding strategy, which is able to shift the demand, from high price periods to low price periods, has been obtained by solving two subproblems. The effectiveness of the proposed method has been tested on a 5-bus system and modified IEEE 30-bus system considering the hourly day-ahead market. Results obtained with the PRDS based bidding strategy have been compared with those obtained with a Conventional Price-Quantity (CPQ) bid. In simulation studies, it is observed that the PRDS approach can control the LMPs and congestion at the system buses. It is also found that PRDS can mitigate the market power by flattening the demand, which led to more saving and satisfying demand.

Price Responsive Demand Management of an Industrial Buyer in Day-ahead Electricity Market

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Arvind Kumar Jain ◽  
S. C. Srivastava
Keyword(s):  
Cost Saving ◽  
Electricity Market ◽  
Demand Management ◽  
Demand Forecasting ◽  
High Price ◽  
Bidding Strategy ◽  
Transmission Congestion ◽  
Market Clearing ◽  
Purchase Cost ◽  
Bus System

Abstract An industrial buyer, who can reschedule its production plan, may develop strategic bid by shifting its demand from high price period to low price period. Based on this concept, a new optimization formulation has been proposed to develop bidding strategy of such buyers considering Price Responsive Demand Shifting (PRDS). Since the bidding strategy of the buyer depends upon the market clearing price, which is volatile/uncertain due to various factors like gaming behavior of participants, demand forecasting error, transmission congestion etc, the proposed optimization problem is formulated as a stochastic linear problem, comprising of two sub-problems. The first sub-problem represents the market clearing process by the System Operator, which is formulated to maximize the social welfare of the market participants, while the second sub-problem aims at maximizing the purchase cost saving of the industrial buyer. The optimal bidding strategy has been obtained by solving these two sub-problems considering hourly market clearing for 24-hour scheduling period. The effectiveness of the proposed method has been tested on a 5-bus system and modified IEEE 30-bus system. Results obtained with the demand shifting based bidding strategy have been compared with those obtained with a Conventional Price Quantity (CPQ) bid strategy. It has been observed that the proposed approach leads to enhancement in the purchase cost saving as compared to the CPQ and meets the energy consumption targets of the industrial buyer.

A Novel Optimization Algorithm for Transient Stability Constrained Optimal Power Flow

2016 ◽  
pp. 147-176
Author(s):  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
New England ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Linear Optimization ◽  
Standard Test ◽  
Optimal Power ◽  
Teaching Learning ◽  
Bus System

Optimal power flow with transient stability constraints (TSCOPF) becomes an effective tool of many problems in power systems since it simultaneously considers economy and dynamic stability of power system. TSC-OPF is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. This paper presents a novel and efficient optimisation approach named the teaching learning based optimisation (TLBO) for solving the TSCOPF problem. The quality and usefulness of the proposed algorithm is demonstrated through its application to four standard test systems namely, IEEE 30-bus system, IEEE 118-bus system, WSCC 3-generator 9-bus system and New England 10-generator 39-bus system. To demonstrate the applicability and validity of the proposed method, the results obtained from the proposed algorithm are compared with those obtained from other algorithms available in the literature. The experimental results show that the proposed TLBO approach is comparatively capable of obtaining higher quality solution and faster computational time.

Impacts of Emission Trading on Optimal Bidding Strategies of Generation Companies in Day-Ahead Electricity Markets

2014 ◽  
Vol 521 ◽  
pp. 476-479 ◽  
Author(s):  
Guo Zhong Liu
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Emissions Trading ◽  
Emission Trading ◽  
Distribution Functions ◽  
Bidding Strategy ◽  
Chance Constrained Programming ◽  
Optimization Approach ◽  
Generation Company ◽  
Optimal Bidding

The impacts of Emission trading on building the optimal bidding strategy for a generation company participating in a day-ahead electricity market is investigated. The CO2 emission price in an emissions trading market is evaluated by using an optimization approach similar to the well-developed probabilistic production simulation method. Then upon the assumption that the probability distribution functions of rivals bidding are known, a stochastic optimization model for building the risk-constrained optimal bidding strategy for the generation company in the framework of the chance-constrained programming is presented. Finally, a numerical example is served for demonstrating the feasibility of the developed model and method, and the optimal bidding results are compared for the two situations with and without the CO2 emissions trading.

Strategic Bidding and Risk Assessment Using Genetic Algorithm in Electricity Markets

2009 ◽  
Vol 10 (5) ◽  
Author(s):  
Arvind Kumar Jain ◽  
S.C. Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Electricity Markets ◽  
Electricity Market ◽  
Value At Risk ◽  
Financial Risk ◽  
Congestion Management ◽  
Bidding Strategy ◽  
Strategic Bidding ◽  
Optimal Bidding ◽  
Sensitivity Factors

In an electricity market, suppliers are more concerned with maximizing their profit and minimizing the financial risk, which can be achieved through strategic bidding. In this paper, Equal Incremental Cost Criteria (EICC) has been used for developing the optimal bidding strategy. The rival's bidding behavior has been formulated using a stochastic optimization model. Genetic Algorithm (GA), along with ac sensitivity factors, has been used to decide the optimal bidding strategy including congestion management to maximize the profit of the suppliers, considering single sided as well as double sided bidding. Both pure as well as probabilistic strategies have been simulated. Results with Sequential Quadratic Programming (SQP), a classical optimization method, and dc sensitivity factors have also been obtained to compare and establish the effectiveness of proposed method. Value at Risk (VaR) has been calculated as a measure of financial risk.

Google Hangout with Annie Baker, Playwright 1/25/16, 2:33 PM

2016 ◽  
Vol 69 (4) ◽  
pp. 57-64
Author(s):  
Genevieve Yue
Keyword(s):  
New York ◽  
New England ◽  
Selection Process ◽  
Pulitzer Prize ◽  
Film Festival ◽  
The Past

Genevieve Yue interviews playwright Annie Baker, whose Pulitzer Prize–winning play The Flick focuses on the young employees of a single-screen New England movie house. Baker is one of the most critically lauded playwrights to emerge on the New York theater scene in the past ten years, in part due to her uncompromising commitment to experimentation and disruption. Baker intrinsically understands that arriving at something meaningful means taking a new way. Accordingly, Baker did not want to conduct a traditional interview for Film Quarterly. After running into each other at a New York Film Festival screening of Chantal Akerman's No Home Movie (2015)—both overwhelmed by the film—Yue and Baker agreed to begin their conversation by choosing a film neither of them had seen before and watching it together. The selection process itself led to a long discussion, which led to another, and then finally, to the Gmail hangout that forms the basis of the interview.

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