A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Abstract

Abstract-Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.

Review of Trends and Targets of Complex Systems for Power System Optimization

“…The OS in [122] is dedicated to increasing the stability of PSs powered by many wind power plants equipped with the doubly fed induction generators. An OS in [123] maximizes the operation stability of the DS powered by the distributed generation units. The OS's algorithm is based on a distributed locational marginal pricing.…”

On Demand Response Modeling and Optimization of Power in a Smart Grid

“…These communication channels can be included in the models by integrating external communication network simulators (Mets et al, 2011), or more directly by utilizing various probability distributions for the relevant parameters (Kilkki et al, 2014). In addition, constraints on locational grid limits might have to be taken into account (Wang et al, 2015).…”

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

“…The components a smart city include smart infrastructure, smart buildings, smart transportation, smart energy, smart health care, smart technology, smart governance, smart education, and smart citizens [1][2][3][4][5], among which the concept of smart energy can be viewed as an "Energy Internet" model [6][7][8][9][10][11]. The backbone of a smart energy system is the smart energy grid, the smart energy metering of which is an important component.…”