Modeling Time of Use Pricing for Load Aggregators Using New Mathematical Programming with Equality Constraints

Ahmadian, Saeed; Malki, Heidar; Sadat, Amin Rahnama

doi:10.1109/codit.2018.8394778

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…The producer negotiates the price at the wholesale level and quantity ordered with the supplier in order to maximize the net profit, as determined by the primary objective function [24], [25]. The net profit of the producer was calculated by subtracting the costs of purchasing, production, transportation, holding, and ordering from the money generated from sales.…”

Section: Supplier Retailer Production Customermentioning

confidence: 99%

The Bi-Level Particle Swarm Optimization for Joint Pricing in a Supply Chain

Mansyuri,

Panudju,

Sitorus

et al. 2024

IJACSA

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This study examines the integration of pricing and lot-sizing strategies within a system comprising only one producer and retailer. The adoption of a bi-level programming technique is justified in establishing a bi-level joint pricing model guided by the producer owing to the hierarchical nature of the supply chain. This problem maximizes manufacturer and retailer profitability by setting the wholesale quantity, lot size, and retail price simultaneously. We created a bi-level particle swarm optimization to solve bi-level programming challenges. This algorithm effectively addresses BLPPS by eliminating the need for any priori assumptions about the conditions of the problem. The bi-level particle swarm optimization algorithm demonstrated a commendable level of efficacy when applied to a set of eight benchmark bi-level issues. The proposed bi-level model was solved using the BPSO and analyzed using experimental data.

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Section: Supplier Retailer Production Customermentioning

confidence: 99%

The Bi-Level Particle Swarm Optimization for Joint Pricing in a Supply Chain

Mansyuri,

Panudju,

Sitorus

et al. 2024

IJACSA

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“…In this context, the authors in Reference 81 proposed a MILP. They evaluated it on load aggregators with different consumers.…”

Section: Optimization Techniques For Energy Tradingmentioning

confidence: 99%

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Alsalloum

Merghem-Boulahia

Rahim

2020

Int Trans Electr Energ Syst

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Summary The smart grid (SG) is an enhancement of the traditional electrical grid. Its main objectives are diverse, focusing mainly on minimizing the energy cost, as well as energy consumption. The smooth integration of renewable energy balances energy production and consumption. Dynamic pricing (DP) becomes one of the important integrated solutions, which comes in line with the global efficiency and reliability of smart grids. DP incentivizes the consumer to participate in the energy scheduling decision, mainly in the real‐time decision. Consumer represents the key challenge in the SG in perspective to make the energy system more efficient. Moreover, the success of smart grids relies mostly on the evolution of the widely used optimization methods. These include measures to encourage consumers and providers to be involved in the development of the SG. The main objective of this article is to review and analyze the recent research works regarding the pricing strategies and optimization methods utilized in the context of SG. A systematic analysis is thus performed by considering twofold views. First, it comprehensively approaches the pricing majors and visualizes the game theory used in real‐time pricing. Second, it describes the optimization methods used in the smart grid context and compares them in terms of their complexity.

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“…Replacing the dual constraints (differentiating with respect to decision variables) with the strong duality condition (at the optimal point) of (9b) leads to a MPEC problem. It must be mentioned that the strong duality for the power system physical constraints are already proven in [24], [25], and [29]. To be specific first we need to introduce the Lagrangian multipliers and Lagrangian function.…”

Section: Correlation Between Market and Compromised Data Using Mpecmentioning

confidence: 99%

Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints

et al. 2019

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With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. In this paper, an attacker is considered a participant in the virtual bidding procedure in the day-ahead (DA) and realtime (RT) electricity markets to maximize its profit. The cyber attacker attempts to identify the optimal power system measurements to attack along with the false data injected into measurement devices. Towards the maximum profit, the attacker needs to specify the relation between manipulated meters, virtual power traded in the markets, and electricity prices. Meanwhile, to avoid being detected by the system operator, the attacker considers the physical power system constraints existing in the DA and RT markets. Then, a bilevel optimization model is presented which combines the real electricity market state variables with the attacker decision-variables. Using the mathematical problem with equilibrium constraints, the presented bilevel model is converted into a single level optimization problem and the optimal decision variables for the attacker are obtained. Finally, simulation results are provided to demonstrate the performance of the attacker, which also provides insights for security improvement. INDEX TERMS Cyber-security, electricity market, false data injection, mathematical programming with equilibrium constraints.

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Modeling Time of Use Pricing for Load Aggregators Using New Mathematical Programming with Equality Constraints

Cited by 11 publications

References 21 publications

The Bi-Level Particle Swarm Optimization for Joint Pricing in a Supply Chain

The Bi-Level Particle Swarm Optimization for Joint Pricing in a Supply Chain

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Modelling Cyber Attacks on Electricity Market Using Mathematical Programming With Equilibrium Constraints

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