This paper integrates the conception of forecasting Renewable Energy (RE) sources and the user's load demands with intelligent Demand Side Management (DSM) under smart DC micro-grid (SDMG) architecture. The RE are mainly consisting of intermittent solar and wind generators, while the load demands are classified as base (uncontrollable) loads and flexible (controllable) loads. The base loads are priority loads and are served in real time, while flexible loads could be operated intelligently according to the availability of the supply. We integrate a day-ahead prediction mechanism for RE, so that we can schedule a day-ahead consumption accordingly. Practically, these predictions are attained with certain level of forecasting errors, causing imbalance in supply and demands at real-time. This imbalance also known as RE uncertainty, will make the power system unstable. To address the dynamic behavior of SDMG and to balance supply and demands, we propose a novel robust control strategy for controllable flexible demands. To simplify our system we make the generation and demands deterministic, by employing intelligence of Support Vector Machine (SVM) learning algorithm. We then incorporate SVM with novel Sliding Mode Control (SMC) for scheduling consumer's flexible loads to make DSM more efficient and accurate. The energy allocation mechanism to consumer demands is made analogous to non-linear fluid flow model. The simulations have established an effective forecasted data using SVM and efficient balancing results of supply and demand using SMC.

An energy-efficient smart home generally integrates renewable energy sources (RESs) with intelligent energy devices (IEDs) to make itself cost-efficient. However, the stochastic nature of the RESs and IEDs makes the power network uncertain, and it degrades the economic efficiency of the smart home. Hence a co-simulation based intelligent home energy management system (HEMS) is proposed in this paper. It interconnects the rolling horizon-based model predictive energy scheduling mechanism with robust control strategies. It manages the load following energy supply entities (ESEs) optimally and efficiently. A time-ahead scheduler integrates an optimal algorithm to estimate the cost-optimal (e.g., minimizing total energy cost) decision signals for various ESEs by incorporating component based losses and efficiencies. Besides, a real-time distributed robust control strategy is established executing a coordinated energy sharing mechanism by incorporating an auxiliary power source. Time-triggered low-jitter wireless communication architecture is adopted to transfer the optimal decision signals from the central scheduler to the distributed device level local controller. A scenario based comparison of various AC/DC models of a home area power network (HAPN) evaluates the strength of the prospective HEMS.

Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making time-ahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with grid-supplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a “min-max” optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home.

To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative $13\%$ cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to $0.013\%$.

In this paper, an evaluation theory of hybrid model for short-term electricity load forecasting is presented using simple soft-technique of predicting data. A model that integrates fuzzy system with neural network database is demonstrated and eventually compared with a traditional statistical method of linear regression. Power load forecasting errors especially for weekends, which is much higher than that of weekdays, is reduced using the probabilistic and stochastic natured Hybrid Adaptive Fuzzy Neural System (HAFNS) method. Neural network database uses temperature and power loads as predictors to train the data sets and then use fuzzy system to develop membership functions, forecasting future power load demands for subsequent hours. HAFNS model is made using power load and temperature data of 2015. The training and testing set of HAFNS is composed of yearly data, which may be decomposed on monthly, daily and hourly basis for comparison. The simulation results of the forecasted data including error distribution graphs are demonstrated.