Energy Management System for Microgrid System using Improved Grey Wolf Optimization Algorithm

An Energy Management System (EMS) is indispensable to monitor the power flow and load matching inside a microgrid during grid-connected mode (GCM) and islanded modes (IM) of operation. Many conventional optimization algorithms show poor reliability for real time optimization problem solving where an objective function is non-linear. An optimization technique is necessary to reduce the cost of energy obtained from the grid, generated inside the grid, and consumed by the load. This article presents, an optimization scheme based on the improved Grey wolf optimization (GWO) algorithm that considers replacement of wounded/injured wolves of one pack by strong wolves of other pack for an EMS in micro-grid. The GWO optimization algorithm's effectiveness is demonstrated forGCM and IM operation. The proposed GWO shows fast, lost cost and precise optimization of the real time EMS for the grid connected and islanded micro-grid system

Evolutionary Algorithms applied to the Intraday Energy Resource Scheduling in the Context of Multiple Aggregators

The growing number of electric vehicles (EVs) on the road and renewable energy production to meet carbon reduction targets has posed numerous electrical grid problems. The increasing use of distributed energy resources (DER) in the grid poses severe operational issues, such as grid congestion and overloading. Active management of distribution networks using the smart grid (SG) technologies and artificial intelligence (AI) techniques by multiple entities. In this case, aggregators can support the grid's operation, providing a better product for the end-user. This study proposes an effective intraday energy resource management starting with a day-ahead time frame, considering the uncertainty associated with high DER penetration. The optimization is achieved considering five different metaheuristics (DE, HyDE-DF, DEEDA, CUMDANCauchy++, and HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators with variations from the day-ahead around the 6 % mark, except for the final aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results.This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI- 01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC),CEECIND/02814/2017, and UIDB/000760/2020.info:eu-repo/semantics/publishedVersio

Impact of demand response management on chargeability of electric vehicles

AbstractLarge-scale penetration of electric vehicles (EVs) would significantly increase the load requirements of buildings in highly urbanized cities. EVs exhibit higher degree of charging flexibility when compared to other interruptible loads in buildings. Hence, EVs can be assigned lower priority and interrupted before interrupting any other loads. Any temporary interruption will have minimum impact on EV owner's satisfaction/comfort. However, it should be ensured that the EVs could be charged to the owner's required state of charge (SOC) by the time of departure. The scheduling algorithms that are used to manage the EV charging process ensure that the charging requirements are fulfilled even when there are temporary interruptions. The capability of the scheduling algorithms to manage mismatches decreases with the decrease in time available for charging. In this paper, the impact of demand response management (DRM) on the chargeability of the EVs while using different priority criteria is examined. Subsequently, the proportion of interruption for each EV with different priority criteria and the need for determining the chargeability of EVs before shedding them are studied. A scheduling driven algorithm is proposed which can be used for determining the chargeability of EVs and can be used in combination with DRM

Heuristic-based shiftable loads optimal management in smart micro-grids

In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original extension of a glowworm swarm particles optimization algorithm, with algorithmic enhancements to treat multiple objective formulations. The performance of the algorithm is compared to the NSGA-II on the considered power systems application

Mathematical models and analysis for demand side management in residential electricity distribution networks.

Development of smart grids along with communication technologies have led to the increased attention and adoption of demand side management (DSM) in the residential sector. Among various DSM schemes, demand response (DR) is a market- based mechanism to shave peak electricity consumption at the system level. In the past decade, the academia has seen a growing literature studying load management methodologies for residential consumers. A typical demand response program has three important facets: the energy cost, comfort of the consumers and overall system efficiency. In this dissertation, we investigate and develop models for effective load control to minimize energy cost and for understanding electricity consumer behavior so as to best design DR schemes. Participation in a real-world field demonstration not only stimulated our motivation for these studies, but also provided us with real- world data to validate the developed models and analyses. This in fact makes the dissertation distinct from current academic literature. We first develop a control algorithm for Heating Ventilation Air-Conditioning (HVAC) systems in households during a peak period. The dynamic programming based model can determine the optimal temperature set-points of a thermostat given the lower and upper limits of temperature that household feels comfortable and the desired duration of the control. The temperature limits act as a quantitative metric for the comfort level of consumers. The objective is to minimize the energy consumption. The model is particularly suitable for DR programs with critical peak pricing, in which a higher electricity rate occurs during the peak period. When deployed separately during the peak and adjoining two periods, the model can keep the inside temperature within the given limits while consuming minimal energy during the peak period. This ensures that the HVAC system would have minimal usage during the peak period as the temperature is kept within the limits. In addition, we show that alternative start and end times of the control algorithm can be tested for each home. Analyses of the alternative options provide us with information about the insulation of the building. We perform computational experiments with real-world data to show the efficacy of the proposed methodology. Second, we propose a mixed-integer linear fractional programming (MILFP) model to optimally deploy the dynamic programming based HVAC controllers among a pool of homes in a staggered fashion. Doing so, the model aims to flatten the demand curve over time thus maximizing the load factor for the entire distribution network. In addition, we develop a reformulation of the MILFP model into an MILP model which significantly reduces computational time for medium-scale instances. Furthermore, for large-scale instances, excessive computational times by general purpose solvers motivate us to develop a customized bi-section search algorithm. Our extensive computational experiments conclude that the customized algorithm is able to solve real-world as well as randomly generated instances in reasonable CPU times. In another effort, we study the behavior of consumers when subject to dynamic pricing under a DR program. We model the price-responsive behavior with utility functions and develop a bi-level programming model to estimate the coefficients of such a function utilizing consumption data from advanced metering infrastructure (AMI) from the field demonstration project mentioned previously. The upper level objective is to minimize the estimation error between the measured data and the optimum consumption while the lower level is for each household/consumer to maximize their total utility of energy consumption. We propose a trust-region algorithm to solve the non-linear bi-level utility estimation (BLUE) model after employing linear and quadratic approximation for the upper and lower level objective function, respectively. A mathematical property of the optimal solution is exploited to develop a cut that has significantly improved the computational time. Numerical experiments with real world data are conducted to validate the proposed models. In addition, we show the strong positive correlation between the utility coefficients and the widely used price elasticity property. Finally, this dissertation also presents several empirical models to assess the effect of smart technologies on electricity consumption under a demand charge dynamic pricing rate. The models developed here were being utilized in the aforementioned demand response pilot study. We present a statistical test based model to estimate the change of coincident load of residential consumers with the installation of efficient appliances including heat pump water heaters, smart thermostats, and battery storage units. The method utilizes a day matching algorithm to pair days with similar weather conditions. The consumption data from the two paired up days are used to conduct a paired t-test to evaluate the statistical significance of the changes. The results reveal that insulation plays an important role in energy savings along with battery systems

Optimal Home Energy Management System for Committed Power Exchange Considering Renewable Generations

This thesis addresses the complexity of SH operation and local renewable resources optimum sizing. The effect of different criteria and components of SH on the size of renewable resources and cost of electricity is investigated. Operation of SH with the optimum size of renewable resources is evaluated to study SH annual cost. The effectiveness of SH with committed exchange power functionality is studied for minimizing cost while responding to DR programs

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households