Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (4)

Search Parameters:
Keywords = Raiffa–Kalai–Smorodinsky bargaining solution

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
13 pages, 836 KB  
Article
The Raiffa–Kalai–Smorodinsky Solution as a Mechanism for Dividing the Uncertain Future Profit of a Partnership
by Yigal Gerchak and Eugene Khmelnitsky
Games 2025, 16(3), 29; https://doi.org/10.3390/g16030029 - 4 Jun 2025
Viewed by 683
Abstract
Establishing a partnership necessitates agreeing on how to divide future profits or losses. We consider parties who wish to contract on the division of uncertain future profits. We propose to divide profits according to the Raiffa–Kalai–Smorodinsky (K-S) solution, which is the intersection point [...] Read more.
Establishing a partnership necessitates agreeing on how to divide future profits or losses. We consider parties who wish to contract on the division of uncertain future profits. We propose to divide profits according to the Raiffa–Kalai–Smorodinsky (K-S) solution, which is the intersection point of the feasible region’s boundary and the line connecting the disagreement and ideal points. It is the only function which satisfies invariance to linear transformations, symmetry, strong Pareto optimality, and monotonicity. We formulate the general problem of designing a contract which divides uncertain future profit between the partners and determines shares of each partner. We first focus on linear and, later, non-linear contracts between two partners, providing analytical and numerical solutions for various special cases in terms of the utility functions of the partners, their beliefs, and the disagreement point. We then generalize the analysis to any number of partners. We also consider a contract which is partially based on the parties’ financial contribution to the partnership, which have a positive impact on profit. Finally, we address asymmetric K-S solutions. K-S solutions are seen to be a useful predictor of the outcome of negotiations, similar to Nash’s bargaining solution. Full article
Show Figures

Figure 1

17 pages, 1194 KB  
Article
Finding Multiple Equilibria for Raiffa–Kalai–Smorodinsky and Nash Bargaining Equilibria in Electricity Markets: A Bilateral Contract Model
by Reinaldo C. Garcia, Javier Contreras, Bárbara Caldeira Macedo, Daniel da Silva Monteiro and Matheus L. Barbosa
Designs 2021, 5(1), 3; https://doi.org/10.3390/designs5010003 - 2 Jan 2021
Cited by 2 | Viewed by 3247
Abstract
In a deregulated market, energy can be exchanged like a commodity and the market agents including generators, distributors, and the end consumers can trade energy independently settling the price, volume, and the supply terms. Bilateral contracts (BCs) have been applied to hedge against [...] Read more.
In a deregulated market, energy can be exchanged like a commodity and the market agents including generators, distributors, and the end consumers can trade energy independently settling the price, volume, and the supply terms. Bilateral contracts (BCs) have been applied to hedge against price volatility in the electricity spot market. This work introduces a model to find all solutions for the equilibria implementing the Raiffa–Kalai–Smorodinski (RKS) and the Nash Bargaining Solution (NBS) approaches in an electricity market based on BCs. It is based on creating “holes” around an existing equilibrium within the feasibility set, yielding a new (smaller) feasibility set at each iteration. This research has two players: a generation company (GC) and an electricity supplier company (ESC), aiming to achieve the highest profit for each of them. The results present all possible RKS and NBS, in addition to showing all assigned energies for a case study at different time frames. The multiple equilibria solutions allow the ESC and the GC to apply different strategies knowing that they can still achieve an optimal solution. Full article
Show Figures

Figure 1

14 pages, 776 KB  
Article
Raiffa-Kalai-Smorodinsky Bargaining Solution for Bilateral Contracts in Electricity Markets
by Reinaldo Crispiniano Garcia, Javier Contreras, Matheus de Lima Barbosa, Felipe Silva Toledo and Paulo Vinicius Aires da Cunha
Energies 2020, 13(9), 2397; https://doi.org/10.3390/en13092397 - 11 May 2020
Cited by 9 | Viewed by 3865
Abstract
In electricity markets, bilateral contracts (BC) are used to hedge against price volatility in the spot market. Pricing these contracts requires scheduling from either the buyer or the seller aiming to achieve the highest profit possible. Since this problem includes different players, a [...] Read more.
In electricity markets, bilateral contracts (BC) are used to hedge against price volatility in the spot market. Pricing these contracts requires scheduling from either the buyer or the seller aiming to achieve the highest profit possible. Since this problem includes different players, a Generation Company (GC) and an Electricity Supplier Company (ESC) are considered. The approaches to solve this problem include the Nash Bargaining Solution (NBS) equilibrium and the Raiffa–Kalai–Smorodinsky (RKS) bargaining solution. The innovation of this work is the implementation of an algorithm based on the RKS equilibrium to find a compromise strategy when determining the concessions to be made by the parties. The results are promising and show that the RKS approach can obtain better results compared to the Nash equilibrium method applied to a case study. Full article
(This article belongs to the Section C: Energy Economics and Policy)
Show Figures

Figure 1

17 pages, 2091 KB  
Article
Economic Dispatch with Demand Response in Smart Grid: Bargaining Model and Solutions
by Kai Ma, Congshan Wang, Jie Yang, Qiuxia Yang and Yazhou Yuan
Energies 2017, 10(8), 1193; https://doi.org/10.3390/en10081193 - 12 Aug 2017
Cited by 17 | Viewed by 5245
Abstract
This paper proposes an economic dispatch strategy for the electricity system with one generation company, multiple utility companies and multiple consumers, which participate in demand response to keep the electricity real-time balance. In the wholesale markets, multiple utility companies will commonly select a [...] Read more.
This paper proposes an economic dispatch strategy for the electricity system with one generation company, multiple utility companies and multiple consumers, which participate in demand response to keep the electricity real-time balance. In the wholesale markets, multiple utility companies will commonly select a reliable agent to negotiate with the generation company on the wholesale price. It is challengeable to find a wholesale price to run the electricity market fairly and effectively. In this study, we use the multiple utility companies’ profits to denote the utility function of the agent and formulate the interaction between the agent and the generation company as a bargaining problem, where the wholesale price was enforced in the bargaining outcome. Then, the Raiffa–Kalai–Smorodinsky bargaining solution (RBS) was utilized to achieve the fair and optimal outcome. In the retail markets, the unfavorable disturbances exist in the power management and price when the consumers participate in the demand response to keep the electricity real-time balance, which motivates us to further consider the dynamic power management algorithm with the additive disturbances, and then obtain the optimal power consumption and optimal retail price. Based on the consumers’ utility maximization, we establish a price regulation model with price feedback in the electricity retail markets, and then use the iterative algorithm to solve the optimal retail price and the consumer’s optimal power consumption. Hence, the input-to-state stability condition with additive electricity measurement disturbance and price disturbance is given. Numerical results demonstrate the effectiveness of the economic dispatch strategy. Full article
(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)
Show Figures

Figure 1

Back to TopTop