Hourly load shifting approach for demand side management in smart grid using grasshopper optimisation algorithm
01 Mar 2020-Iet Generation Transmission & Distribution (The Institution of Engineering and Technology)-Vol. 14, Iss: 5, pp 808-815
TL;DR: A new approach has been proposed for the demand side management, which is based on shifting a load from peak to off-peak time, and shows a significant reduction in peak hour demand and utility bills.
Abstract: In this new era of communication, the advent of the smart grid has revolutionised the power system network. The goal of smart grids is to provide a more reliable, environment-friendly and economically efficient power system. Demand side management or demand side response is one of the key components of the smart grid which accomplishes the smart grid that would provide intelligence to the traditional grid. Here, a new approach has been proposed for the demand side management, which is based on shifting a load from peak to off-peak time. The main objective of the work is to reduce the peak hour demand and the utility bill of the consumers. To achieve these objectives, the proposed strategy is modelled as a minimised optimisation problem and it tries to find out the optimal solution. For that, two optimisation algorithms, the first one is particle swarm optimisation algorithm and the second one is grasshopper optimisation algorithm, are proposed and applied in three area loads of the smart grid, i.e. residential, commercial and industrial. The obtained simulation results show a significant reduction in peak hour demand and utility bills.
Citations
More filters
TL;DR: A novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation is proposed.
Abstract: Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.
32 citations
TL;DR: In this paper, a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids is presented, and several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system.
Abstract: This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.
23 citations
TL;DR: A hybrid technique based on DSM and MAS to manage the energy in residential, commercial, and industrial microgrids and its capability of saving energy cost and reducing peak demands is proposed.
Abstract: The demand-side management (DSM) has a great significance as it covers economical activities through decreasing demand of electricity during high consumption hours. Meanwhile, the multi-agent system (MAS) is a computational intelligence technique that has been applied to reduce the overall consumers’ cost as well as total loads. This paper proposes a hybrid technique based on DSM and MAS to manage the energy in residential, commercial, and industrial microgrids. First, the DSM mechanism based on load shifting technique for managing and coordinating load demand is implemented. Then, according to the resulting scheduled load curve, the MAS is utilized in order to optimally coordinate different renewable sources to cover the demand and allowable energy to exchange between the main grid and microgrids. The problem is formulated as an optimization problem and solved by a powerful Antlion optimizer algorithm with an objective of maximization of load factor and minimization of overall consumers’ cost. The validation of the proposed algorithm is achieved via comparative studies with other published works. The results show the prosperity of the proposed technique and its capability of saving energy cost and reducing peak demands.
9 citations
TL;DR: In this paper, a high-accuracy assessment of domestic demand-side management (DSM) approach in the context of distributed renewable energy sources (RES) was provided, where a microgrid model of a typical UK residential estate was developed to simulate the impact of RES.
Abstract: This paper provides a high-accuracy assessment of domestic demand-side management (DSM) approach in the context of distributed renewable energy sources (RES). To determine the potential of domestic DSM for households, a microgrid model of a typical UK residential estate was developed to simulate the impact of RES. The microgrid model comprises 15 UK households with appropriate allocation of washing machines, tumble dryers and dishwashers in accordance with the statistical data. In order to obtain a high-accuracy result, the power consumption of the microgrid model utilises real historical high-resolution data of household energy consumption and RES generation. Thereafter, 40% of distributed wind and solar energy is implemented in the model to produce two individual scenarios. The operation of the white appliances in the model is controlled using a domestic DSM based on a load shifting algorithm. The primary criterion of the DSM considered in this paper is the reduction in energy feedback to the grid in order to decrease the utilisation of the grid and to reduce the transmission losses. The results obtained from the model simulation are compared to the baseline model and discussed with respect to the possible benefits of implementation of domestic DSM under the impact of RES. It has been shown that the self-consumption ratio of the microgrid operating under the DSM is increased by 3% for both scenarios. The model analysis provides highly realistic results which can be used for efficiency assessment of various load shifting methods.
6 citations
24 Sep 2020
TL;DR: In this article, modified coefficient c for fine tuning these to contradictory process while searching for optimum solution is introduced. And the anticipated algorithm is named as modified GOA (mGOA) and tested over a standard set of benchmark problems.
Abstract: The grasshopper optimization algorithm (GOA) mimics the foraging behavior of grasshopper insects. It is one of the youngest and widespread algorithms for optimization. In GOA exploration and exploitation depends on coefficient c used in position update process. So as to improve balancing in exploration and exploitation this paper introduced modified coefficient c for fine tuning these to contradictory process while searching for optimum solution. The new value of c is decided adaptively and stimulated by hyperbolic function. The anticipated algorithm is named as modified GOA (mGOA) and tested over a standard set of benchmark problems. Outcomes proves that mGOA outperformed considered algorithm for more than 90% problems.
4 citations
References
More filters
04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
14,477 citations
TL;DR: The proposed grasshopper optimisation algorithm is able to provide superior results compared to well-known and recent algorithms in the literature and the results of the real applications prove the merits of GOA in solving real problems with unknown search spaces.
1,796 citations
TL;DR: A heuristic-based Evolutionary Algorithm that easily adapts heuristics in the problem was developed for solving this minimization problem and results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
1,070 citations
TL;DR: In this article, the implementation of both point of collapse (PoC) and continuation methods for the computation of voltage collapse points (saddle-node bifurcations) in large AC/DC power systems is described.
Abstract: The implementation of both point of collapse (PoC) methods and continuation methods for the computation of voltage collapse points (saddle-node bifurcations) in large AC/DC power systems is described. The performance of these methods is compared for real systems of up to 2158 buses. Computational details of the implementation of the PoC and continuation methods are detailed, and the unique problems encountered due to the presence of high-voltage direct-current (HVDC) transmission, area interchange power control, regulating transformers, and voltage and reactive power limits are discussed. The characteristics of a robust PoC power flow program are presented, and its application to detection and solution of voltage stability problems is demonstrated. >
614 citations
TL;DR: The RTS is extended by including more factors and system conditions which may be included in the reliability evaluation of generating systems to create a wider set of consistent data.
Abstract: This paper outlines some of the restrictions which currently exist in the generation data of the IEEE Reliability Test System (RTS) The paper extends the RTS by including more factors and system conditions which may be included in the reliability evaluation of generating systems These extensions create a wider set of consistent data The paper also includes generation reliability indices for the base and extended RTS These indices have been evaluated without any approximations in the evaluation process and therefore provide a set of exact indices against which the results from alternative and approximate methods can be compared
167 citations