A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

Abstract

This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle.

Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market

“…In [10][11][12], a transactive EMS that considers selling prosumer's energy to the grid for demand-side management was introduced. Daneshvar et al [10] designed four mathematical models of microgrids using a transactive EMS.…”

“…The main contribution of [11] is on proposing a transactive EMS to mitigate grid overloading in a two-stage management model. Koltsaklis et al [12] designed a transactive EMS similar to [11] that starts in the day-ahead stage. The main contribution of [12] is that they implemented three short-term forecasting processes for the optimal day-ahead energy scheduling.…”

“…Koltsaklis et al [12] designed a transactive EMS similar to [11] that starts in the day-ahead stage. The main contribution of [12] is that they implemented three short-term forecasting processes for the optimal day-ahead energy scheduling.…”

“…The common point of transactive systems [10][11][12][13][14][15] is the design of the management system of LEM in a system-friendly manner. The energy of prosumers is shared and managed by a centralized agent or aggregator.…”

Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies

“…Many researches have concentrated on charging optimization and control problems. The authors in [28][29][30] investigated the optimal energy management and control problems of smart home with PEVs and photovoltaic arrays. By designing a smart-charging scheme for PEV, it was shown in [31] that the aggregate EVs are able to reduce the peak demand or peak shaving.…”