Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review

The current era in sustainable development is focused on the rapid integration of renewable energy sources driven by a wide range of socio-economic objectives. Due to the inherent property of time-varying weather conditions, the intermittent sources, that is, Solar PV and Wind Energy, are considered as variable energy resources. The uncertainty and variability problem of these sources has brought many complications to distributed network operators to operate and control the complex or multi-microgrids with limited fast-ramping resources in order to maintain the power system flexibility. It led many researchers to find an alternative strategy since the conventional approaches are no longer adequate to handle the economic implications of operational decision making. At first, the brief review of various deterministic and probabilistic approaches, stochastic programming and robust optimisation strategies to address the uncertainty of variable energy resources are discussed. Furthermore, in the energy management point of view, the optimal scheduling problem of distributed sources of the microgrid is considered, and a brief review of optimisation models, advanced control strategies and demand response strategies to maximise economic benefits of microgrids are also elaborately presented. Finally, the multiagent-based distributed and decentralised control strategies for seamless integration of distributed generator units are reviewed under various configurations of the power grid along with communication network topologies.     

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

Priority and delay constrained demand side management in real‐time price environment with renewable energy source

In smart grid, integration of renewable energy sources such as solar and wind is a challenging task because of their intermittent nature. Most of the existing demand side management techniques are based on day‐ahead pricing or time of use pricing that deviate from real‐time pricing because of unpredictable energy consumption trends and electricity prices. This paper presents opportunistic scheduling algorithms in a real‐time pricing environment based on optimal stopping rule. We classify different users and assign priorities based on energy demand. In order to minimize the electricity bill and appliance waiting time cost, we modify the first come first serve scheduling algorithm. Regarding comfort maximization, priority enable early deadline first scheduling algorithm is proposed, which schedules the appliances based on minimum length of operation time and priority constraints. Simulation results validate the effectiveness of the proposed algorithms in terms of electricity cost reduction and user comfort maximization. Copyright © 2016 John Wiley & Sons, Ltd.     

A comprehensive energy solution for households employing a micro combined cooling,heating and power generation system

In recent years, micro combined cooling, heating and power generation (mCCHP) systems have attracted much attention in the energy demand side sector. The input energy of a mCCHP system is natural gas, while the outputs include heating, cooling and electricity energy. The mCCHP system is deemed as a possible solution for households with multiple energy demands. Given this background, a mCCHP based comprehensive energy solution for households is proposed in this paper. First, the mathematical model of a home energy hub (HEH) is presented to describe the inputs, outputs, conversion and consumption process of multiple energies in households. Then, electrical loads and thermal demands are classified and modeled in detail, and the coordination and complementation between electricity and natural gas are studied. Afterwards, the concept of thermal comfort is introduced and a robust optimization model for HEH is developed considering electricity price uncertainties. Finally, a household using a mCCHP as the energy conversion device is studied. The simulation results show that the comprehensive energy solution proposed in this work can realize multiple kinds of energy supplies for households with the minimized total energy cost.     

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.     

Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices

This paper proposes a new probabilistic framework based on 2m Point Estimate Method (2m PEM) to consider the uncertainties in the optimal energy management of the Micro Girds (MGs) including different renewable power sources like Photovoltaics (PVs), Wind Turbine (WT), Micro Turbine (MT), Fuel Cell (FC) as well as storage devices. The proposed probabilistic framework requires 2m runs of the deterministic framework to consider the uncertainty of m uncertain variables in the terms of the first three moments of the relevant probability density functions. Therefore, the uncertainty regarding the load demand forecasting error, grid bid changes and WT and PV output power variations are considered concurrently. Investigating the MG problem with uncertainty in a 24 h time interval with several equality and inequality constraints requires a powerful optimization technique which could escape from the local optima as well as premature convergence. Consequently, a novel self adaptive optimization algorithm based on θ-Particle Swarm Optimization (θ-PSO) algorithm is proposed to explore the total search space globally. The θ-PSO algorithm uses the phase angle vectors to update the velocity/position of particles such that faster and more stable convergence is achieved. In addition, the proposed self adaptive modification method consists of three sub-modification methods which will let the particles choosel the modification method which best fits their current situation. The feasibility and satisfying performance of the proposed method is tested on a typical grid-connected MG as the case study.