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.

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.

scholarly journals Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

2021 ◽  
Vol 11 (15) ◽  
pp. 6787
Author(s):  
Jože M. Rožanec ◽  
Blaž Kažič ◽  
Maja Škrjanc ◽  
Blaž Fortuna ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Automotive Industry ◽  
Demand Management ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Forecast Errors ◽  
Manufacturing Companies ◽  
Case Scenario ◽  
Crucial Component

Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

2021 ◽  
pp. 0958305X2110148
Author(s):  
Mojtaba Shivaie ◽  
Mohammad Kiani-Moghaddam ◽  
Philip D Weinsier
Keyword(s):  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Power Grid ◽  
Mathematical Formulation ◽  
Test System ◽  
Bidding Strategy ◽  
Level Problem ◽  
Optimal Bidding ◽  
Environmental Measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

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