Optimasi Respon Beban Berbasis Insentif untuk Beban Rumah Tangga Menggunakan Algoritma Symbiotic Organism Search

Pada program Demand Response (DR) yang dilakukan pada penelitian tugas akhir ini bertujuan untuk mengendalikan pola konsumsi daya listrik pada sektor rumah tangga menggunakan algortima symbiotic organism search. Melalui program DR berbasis insentif ini pengguna rumah tangga dapat peralatan listrik dapat dikontrol sehingga beban puncak akan berkurang, stabilitas dan efisiensi jaringan akan meningkat. Peralatan elektronik kategori non esensial di alihkan pada jam-jam tidak sibuk agar didapatkan tagihan listrik yang lebih murah. Agar masyarakat bersedia peralatan rumah tangganya dikontrol secara terpusat, maka penyedia energi memberikan program insentif berupa pengurangan harga pada jam-jam beban rendah. Salah satu algoritma Symbiotic Organism Search (SOS) dapat digunakan sebagai metode optimasi program demand response sehingga didapatkan penjadwalan harga dan jadwalan program insentif yang optimal bagi pelanggan. Dalam tugas akhir ini, SOS akan dibandingkan dengan algoritma Genetic Algorithm (GA) untuk metode pembanding untuk mendapatkan hasil yang lebih optimal dalam melakukan penjadwalan beban rumah tangga. Sedangkan teknologi Home Energy Management System (HEMS) digunakan untuk memonitor dan mengontrol peralatan secara real time dan terpusat. Hasil simulasi menggunakan program GA dapat berkurang hingga 10.9%. Sedangkan hasil analisa menggunakan program SOS dapat berkurang hingga 17.5%. Hasil dari simulasi program DR akan diterapkan pada salah satu fitur aplikasi manajemen energi skala rumah tangga. ============================================================================================================================== Demand Response (DR) program carried out in this final project research aims to control the pattern of electric power consumption in the household sector using the symbiotic organism search algorithm. Through this incentive-based DR program household users can control electrical equipment so that peak loads will be reduced, network stability and efficiency will increase. Non-essential electronic equipment is diverted at busy hours to get cheaper electricity bills. In order for the community to be willing to control their household equipment centrally, the energy provider provides an incentive program in the form of price reductions at low load hours. One of the Symbiotic Organism Search (SOS) algorithms can be used as a method of optimizing the demand response program so that price scheduling and optimal incentive program scheduling are obtained for customers. In this final project, SOS will be compared with the Genetic Algorithm (GA) algorithm for comparison methods to get more optimal results in scheduling household expenses. While Home Energy Management System (HEMS) technology is used to monitor and control equipment in real time and centrally. The simulation results using the GA program can be reduced to 10.9%. While the results of the analysis using the SOS program can be reduced by up to 17.5%. The results of the DR program simulation will be applied to one of the features of a household scale energy management application

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A Distributed Demand-Side Management Framework for the Smart Grid

This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios. We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand

Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe