Optimal Operation of Integrated Energy Systems Subject to Coupled Demand Constraints of Electricity and Natural Gas

This paper proposes a hybrid multi-objective optimization and game-theoretic approach(HMOGTA)to achieve the optimal operation of integrated energy systems(IESs)consisting of electricity and natural gas(E&G)utility networks,multiple distributed energy stations(DESs),and multiple energy users(EUs).The HMOGTA aims to solve the coordinated operation strategy of the electricity and natural gas networks considering the demand characteristics of DESs and EUs.In the HMOGTA,a hierarchical Stackelberg game model is developed for generating equilibrium strategies of DESs and EUs in each district energy network(DEN).Based on the game results,we obtain the coupling demand constraints of electricity and natural gas(CDCENs)which reflect the relationship between the amounts and prices of electricity and cooling(E&C)that DESs purchase from utility networks.Furthermore,the minimization of conflicting costs of E&G networks considering the CDCENs are solved by a multi-objective optimization method.A case study is conducted on a test IES composed of a 20-node natural gas network,a modified IEEE 30-bus system,and 3 DENs,which verifies the effectiveness of the proposed HMOGTA to realize fair treatment for all participants in the IES.

Distributed Nash equilibrium seeking with order-reduced dynamics based on consensus exact penalty

In this paper,we consider a networked game with coupled constraints and focus on variational Nash equilibrium seeking.For distributed algorithm design,we eliminate the coupled constraints by employing local Lagrangian functions and construct exact penalty terms to attain multipliers'optimal consensus,which yields a set of equilibrium conditions without any coupled constraint and consensus constraint.Moreover,these conditions are only based on strategy and multiplier variables,without auxiliary variables.Then,we present a distributed order-reduced dynamics that updates the strategy and multiplier variables with guaranteed convergence.Compared with many other distributed algorithms,our algorithm contains no auxiliary variable,and therefore,it can save computation and communication.

Distributed heavy-ball algorithm of Nash equilibrium seeking for aggregative games

In this paper,we consider the Nash equilibrium(NE)seeking problem for aggregative games and design a distributed heavy-ball algorithm to solve it.This algorithm has faster convergence rate than the well-known distributed first-order algorithms for aggregative games.In order to seek the NE,each player needs to exchange information with its neighbours as well as a cen-tral aggregation.For aggregative games,the aggregative term can be either linear or nonlinear in this paper.Furthermore,we consider the generalised Nash equilibrium seeking problem for aggregative games by taking into account the linear coupled constraints among players,and modify our initial algorithm to include game constraints.