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The power system blackouts have occurred in recent years all across the world. Therefore, it is necessary to develop more effective methods for preventing such events. In this paper, a novel effective method is proposed to restore the frequency of power system and make the system stable for major disturbances. To do this, for the first time, the adaptive load shedding and emergency demand response algorithms are combined to improve the power quality in addition to system stability. To evaluate the proposed method, the New England 39-bus standard grid is selected and simulated using DIgSILENT software package and its performance is studied for moderate and large power deficit scenarios. The obtained results prove the superiority of the new method in comparison with the pervious methods from points of view lower power interruption and reducing the probability of power system collapse by decreasing the convergence time and minimizing the frequency drop. [ABSTRACT FROM AUTHOR]

Summary: The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak‐to‐average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions. [ABSTRACT FROM AUTHOR]

This paper proposes a combined model of Multi-Objective Dynamic Economic and Emission Dispatch (MODEED) with Demand Side Management (DSM) to investigate the benefits of DSM on generation side. This model considers a day ahead based load shifting DSM approach. In order to analyse the effect of DSM on the generation side, the objectives of dynamic economic and emission dispatch problem were minimized individually and simultaneously with and without DSM. A test system with six thermal generating units was considered for the validation of the proposed method. In this paper, authors used Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to minimize the objectives of MODEED problem simultaneously. The simulation results of the MOPSO algorithm have also been compared with the non-dominated sorting genetic algorithm (NSGA-II). It is clear from the results that the proposed combined model is able to give benefits to both utilities and generating companies. [ABSTRACT FROM AUTHOR]

• The electrical equipment was divided into time-shifting and non-time-shifting power. • The starting time of time-shifting load was selected as the variable in PSO algorithm. • The battery capacity was selected to meet the demand of the power peak at night. With the continuous expansion of the development scale, photovoltaic integrated greenhouses are becoming an important type of smart grid prosumer. The demand side management (DSM) of energy is becoming increasingly important in terms of satisfying the energy consumption requirements of production and increasing the economic benefits of the same piece of land. By analyzing greenhouse production activities and the operation of electrical equipment, a particle swarm optimization (PSO) scheme is proposed in this paper, which selects the starting time of time-shifting powers as optimization factors and maximizes the income of power generation as the objective function. In addition, this paper analyzes the problem of energy consumption scheduling in greenhouse production. The results show that this scheme helps to optimally schedule the greenhouse energy consumption and achieves maximum profit by ensuring the reasonable operation of the system. The optimization scheme is beneficial to guide the production activities in greenhouses and the construction of photovoltaic integrated greenhouse systems. [ABSTRACT FROM AUTHOR]

The development of home energy management have increased due to energy saving. Instead of Intrusive Load Monitoring (IALM) which requires individual sensor for each appliance, Non-Intrusive Appliance Load Monitoring (NIALM) is an advanced low-cost system that requires fewer sensors and disaggregates load data in a different way. NIALM is a study to determine energy consumption of individual appliances measured at a single power source point. This system disaggregates data from a total power load and analyses power consumption of an appliance so that consumer can monitor the total power usage of a building. This paper reviews several feature extractions, state-of-the-art load signatures and disaggregation algorithms used for appliance recognition in NIALM method. [ABSTRACT FROM AUTHOR]

Nowadays the power generation and transmission are substantial elements for the society, and will definitely play a more important role in the future. In this paper a modeling framework is presented to analyze security constrained composite generation and transmission expansion planning problem in power systems. Despite of the traditional expansion planning approaches where only supply side options are considered, the proposed approach accounts for both supply and demand side management (DSM) options simultaneously. DSM options are incorporated to correct the shape of the load duration curve in terms of peak clipping and load shifting programs. A mixed integer non-linear programming model is developed to find the optimal location and timing of electricity generation/transmission as well as DSM options while the effect of transmission losses are also taken into account. Nonlinearity of the transmission loss terms is eliminated using the piecewise linearization. Motivating from the structure of the model, Benders decomposition (BD) algorithm is devised. Three effective strategies named: valid inequalities, multiple generation cuts and strong high density cut are also employed to improve the convergence of the proposed BD algorithm. The performance of the accelerated BD algorithm is validated via applying it to the modified 6, 21, 48, 57, 118 and 300 bus IEEE reliability test systems. The computational experiments ensure the practicality of the proposed BD algorithm in terms of decreasing the number of iterations and CPU times. [ABSTRACT FROM AUTHOR]

The high penetration of renewable power generators and various loads have brought a great challenge for dispatching energy in a microgrid system. Heating ventilation air conditioning (HVAC) system, as a household appliance with high popularity, can be considered as an effective technology to alleviate energy dispatch issues. This paper presents novel distributed algorithms based on HVAC to solve the demand side management problem, where the microgrid system with HVAC units is considered as a multi-agent system (MAS). The approach provides a desirable operating frequency signal for each HVAC based on the power mismatch value occurring on each local bus. It utilizes demand response of the HVAC units to minimize the supply-demand mismatch, thus reducing the quantity and capacity of energy storage devices potentially to be required. Compared with existing approaches focusing on the distributed algorithms under a fixed communication network, this paper addresses a consensus problem under a switching topology by using the Lyapunov argument. It is verified that a jointly strong and connected topology is a sufficient condition in order to achieve an average consensus for a time-varying topology. A number of cases are studied to evaluate the effectiveness of the algorithms by taking into account its power constraints, dynamic behaviors, anti-damage characteristics and time-varying communication topology. Modelling these system interactions has demonstrated the feasibility of the proposed microgrid system. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a near optimal hoist scheduling and control program for rock winders found in South African deep level mines in the context of demand side management and time-of-use (TOU) tariffs. The objective is to achieve a set hoist target at minimum energy cost within various system constraints. The development of a discrete dynamic and constrained mixed integer linear programming model for a twin rock winder system is presented on which a half-hourly model predictive control (MPC) algorithm containing an adapted branch and bound methodology is applied for near optimal scheduling. Simulation results illustrate the effectiveness of the control program by minimising the energy costs through scheduling according to the TOU tariff and controlling output and ore levels within their boundaries even in the case of significant random delays in the system. Scheduling according to the TOU tariff shows a possible 30.8% reduction in energy cost while approximately 6h of delays in the system resulted in a mere 14% increase in energy cost. [Copyright &y& Elsevier]

Abstract: This paper presents the results of the application of model (developed in part I) and simulation algorithm (developed in part II) for determining the techno-economics of battery storage type hybrid energy system intended to supply the load of a rural remote area having a cluster of nine villages (grid isolated). The hour-by-hour simulation model is intended to simulate a typical one month period of system operation. For simulation purpose, hourly solar insolation data and load data have been generated and used as an input data. Demand side management (DSM) is used in this study to smooth out the daily peaks and fill valleys in the load curve to make the most efficient use of energy sources. The economic analysis has resulted in the calculation of optimized hourly, daily, and monthly system unit cost of proposed hybrid energy system. The obtained results represent also a helpful reference for energy planners in Uttarakhand state and justify the consideration of hybrid energy systems more seriously. [ABSTRACT FROM AUTHOR]