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

Abstract

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.

Efficient hybrid pricing for optimal DSM of home energy management system utilizing load precedence

“…The literature presents a varied spectrum of works addressing different techniques for implementing DR in residential as well as commercial loads and introduces various types of pricing techniques used to achieve the same 1‐8 . DSM is a broad term and DR is a subset of DSM which includes motivation to change the electricity usage by the consumers in response to a pricing signal or through incentive proposed to minimize the electricity usage during peak demand condition or during increased price 9‐11 .…”

“…The tariff structure affects the end user's consumption pattern 4 and works as an effective DSM tool. The current commonly applied dynamic tariff schemes include time‐of‐use (TOU) tariff, real‐time pricing (RTP), critical peak pricing (CPP), etc 5 . All these tariff structures offer variable energy price during the day based on the system demand, that is, higher price during peak demand period.…”

Optimization of Operating Cost and Energy Consumption in a Smart Grid

“…The comparison is drawn for different techniques to reduce consumption and operating costs in an intelligent grid. [20] also described different algorithms, i.e., MOPSO and GA. PSO, MILP, etc., for obtaining objectives. In [21], the total cost of the smart grid is reduced by using the MAPPO algorithm.…”

A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

“…Authors in [35] adopted demand-side management (DSM) strategies like load curtailment and shifting with the ϵ-constraint approach for power usage scheduling to minimize carbon emission and cost for four case studies. Heuristic algorithms like GA, PSO, etc., are adopted in [36] for different aspects of energy optimization. The MAPPO algorithm is adopted in [37] to schedule the load under the RTP scheme for net cost optimization in SPG.…”