Electricity Bills Of Customers Research Articles

A hybrid approach for optimal energy management system of internet of things enabled residential buildings in smart grid

Nowadays, a large number of smart devices in residential buildings is integrated with the evolution of internet of things (IoT), where it is necessary to effectively manage energy to meet the increase in demand. Therefore, in this paper, an optimal energy management strategy using the hybrid Gradient Boosting Decision Tree -Artificial Transgender Longicorn Algorithm (GBDT-ALTA) is proposed for IoT-enabled residential buildings. The main objective of the proposed approach is to minimize the electricity bill of customer, thereby Peak-to-Average Ratio (PAR) is reduced. Due to the scheduling process of residential electric devices, the proposed approach utilizes the waiting time threshold. Here, three types of appliances, such as shiftable electrical devices, thermostatically controlled electrical devices, and generally operated electrical devices are considered. Related to real-time price signals (RTPS) in utilities, scheduling the shiftable electrical devices is processed. The cost minimization with less acceptable time of waiting is achieved by GBDT-ATLA method, which considers the trade-off between the electricity cost and waiting time. Moreover, the proposed approach maintains the stability of the grid, because the stability of grid depends on the PAR. The proposed method is carried out in MATLAB/Simulink site and the simulated results prove that the better performance of GBDT-ATLA approach compared to the existing approaches, like Slime Mould Optimization (SMO), Chaos Game Optimization Algorithm (CGO), and Side-Blotched Lizard Optimization Algorithm (SBLO). The efficiency of the proposed approach under 100, 200, 500, and 1000 trails are 99.7300%, 99.6513%, 99.8363%, and 99.7916% respectively.

A multi-objective residential load management based on self-adapting differential evolution

• An adaptive multi-objective optimization method for demand response is proposed. • Epsilon constrains method is used to get a single point lies within Pareto front. • A tiered incentive can encourage customers to participate in demand response program. • The results indicate significant cost reduction (53.34%) and peak load (43.04%). The current grid system is unable to meet the ever-increasing demands for energy due to annual population growth and the increasing number of energy-consuming devices that are being used in the modern era. One solution that helps to balance available generation capacity with required demand, and increases the grid reliability is the demand response (DR). For this purpose, this paper presents a residential load scheduling algorithm to manage the operation time of household appliances depending on the time of use (ToU) tariff. A self-adapting multi-objective differential evolution (SaMODE) algorithm was developed to find the optimal operation points of home energy appliances, with the aim of minimizing the customer electricity bill and peak load while maintaining the customer's convenience. The proposed tri-objective problem is solved by using the Epsilon-constraint optimization method. In addition, a prior approach is adopted to manage the tradeoff among objectives according to the customer's preferences. The desirable customer preferences are ensured through the prior approach by restricting two objectives to specific values, then finding an optimal corresponding solution for them. The findings and discussion demonstrate that the proposed SaMODE strategy benefits the customers by reducing their energy bill for up to 53.34% with an acceptable level of inconvenience. On the other hand, the power suppliers have also received benefits by decreasing the peak energy demand by up to 43.04%, which in turn increases the stability of the power system and lessen the burden on the utility company.

Fuzzy logic control of hybrid systems including renewable energy in microgrids

With a growing demand for more energy from subscribers, a traditional electric grid is unable to meet new challenges, in the remote areas remains the extension of the conventional electric network very hard to do make prohibitively expensive. Therefore, a new advanced generation of traditional electrical is inevitable and indispensable to move toward an efficient, economical, green, clean and self-correcting power system. The most well-known term used to define this next generation power system is Micro Grid (MG) based on renewable energy sources (RES). Since, the energy produced by RES are not constant at all times, a wide range of energy control techniques must be involved to provide a reliable power to consumers. To solve this problem in this paper we present a Fuzzy Logic Control of isolated Hybrid Systems (HRES) Including Renewable Energy in Micro-Grids to maintain a stability in voltage and frequency output especially in the standalone application. The considered HRES combine a wind turbine (WT) and photovoltaic (PV) panels as primary energy sources and an energy storage system (ESS) based on battery as a backup solution. Simulation results obtained from MATLAB/Simulink environment demonstrate the effectiveness of the proposed algorithm in decreasing the electricity bill of customer.

Optimal management of demand response aggregators considering customers' preferences within distribution networks

In this study, a privacy-based demand response (DR) trading scheme among end-users and DR aggregators (DRAs) is proposed within the retail market framework and by distribution platform optimiser. This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs' and customers' preferences. A bi-level programming model is formulated in a day-ahead market within retail markets. In the upper-level problem, the total operation cost of the distribution system is minimised. The production volatility of renewable energy resources is also taken into account in this level through stochastic two-stage programming and Monte-Carlo simulation method. In the lower-level problem, the electricity bill for customers is minimised for customers. The income from DR selling is maximised based on DR prices through secure communication of household energy management systems and DRA. To solve this convex and continuous bi-level problem, it is converted to an equivalent single-level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper-level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network.

Optimal pricing strategy of power‐retailing companies considering demand response in block bidding markets

Block bidding market (BBM) is a new electricity market mode in the day-ahead market, which is in the stage of development and promotion in China. It is based on the idea that energy should have the same price according to the continuous duration of load. In this study, the authors propose an optimisation model of pricing and purchasing strategy for power-retailing companies in BBMs, based on bilateral contracts with customers and portfolio management of demand response resources. The model consists of an expected profit maximisation problem of supplying energy to end-customers, and a total cost minimisation problem concerning the dispatching of fixed loads and shiftable loads. The proposed model is formulated as a bi-level mixed-integer non-linear program. A load division method is proposed to get better division result by eliminating duration restrictions on divided blocks. The proposed approximate solving algorithm overcomes the diculties in linearisation and gets rid of the inuence of initial value. The strategy is applied to an actual case and is simulated based on real data. Simulation results show that the proposed model has great peak-shaving effects, and can effectively bring more profit to the power-retailing companies while reducing electricity bills of customers.

Smart Home Cyberattack Detection Framework for Sponsor Incentive Attacks

Sponsor incentive pricing is an emerging pricing scheme in smart home energy systems. It allows a supplier of smart appliances to co-pay the electricity bills of customers due to using their products, which boosts the sales of smart appliances in the sponsor program. More importantly, it facilitates to shift energy usage of incentive customers from peak to non-peak hours by designing the time-varying rate, which is inversely proportional to the total demand. Despite its effectiveness in bill reduction and load balancing, the sponsor incentive pricing scheme is vulnerable to various forms of cyberattacks. In this paper, we develop the first detection technique for cyberattacks that exploits the sponsor incentive pricing scheme. Our short-term detection algorithm uses binary logistic regression for learning the energy usage patterns and identifying anomaly ones. Leveraging the partially observable Markov decision process framework, our long-term detection algorithm optimizes the utility’s decision of on-site inspections while minimizing the labor cost and the financial loss due to cyberattacks. The simulation results demonstrate that, for detection of rebate rate cyberattacks, the proposed algorithm reduces the peak-to-average ratio of the aggregate energy usage profile by 26.57% and 9.58%, compared with the no-detection scenario and a natural heuristic detection technique, respectively. For detection of sponsor incentive abuse, the maliciously elevated electricity bill is reduced by 46.03% and 15.30%, respectively.

Default supply auctions in electricity markets: Challenges and proposals

This paper studies premiums got by winning bidders in default supply auctions, and speculation and hedging activities in power derivatives markets in dates near auctions. Data includes fifty-six auction prices from 2007 to 2013, those of CESUR in the Spanish OMEL electricity market, and those of Basic Generation Service auctions (PJM-BGS) in New Jersey's PJM market. Winning bidders got an average ex-post yearly forward premium of 7% (CESUR) and 38% (PJM-BGS). The premium using an index of futures prices is 1.08% (CESUR) and 24% (PJM-BGS). Ex-post forward premium is negatively related to the number of bidders and spot price volatility. In CESUR, hedging-driven trading in power derivatives markets predominates around auction dates, but in PJM-BGS, speculation-driven trading prevails. The policy recommendation to market regulators and administrators is that they should gauge consumers’ price risk aversion before introducing alternative methods to default supply auctions for the computation of the part of cost of energy of the electricity bill of customers whose contracted capacity is small and are not served by other suppliers.

Who pays, who benefits? The financial impacts of solar photovoltaic systems and air-conditioners on Australian households

Increasingly, there are calls for the owners of photovoltaic (PV) systems to pay additional charges on the basis that they are not contributing their fair share to network revenue. Air conditioners (A/Cs) are even more widespread than distributed PV systems, and their use has increased demand peaks and the size of networks required to meet them, the cost of which is typically recovered from all customers. There appears to be limited analysis in the literature regarding the impacts of A/C and PV on the electricity bills of customers who do not have these technologies. While the impacts of renewable energy on centralised electricity generation have been explored in the literature, this paper proposes a methodology to estimate the financial impacts of PV and A/C that flow through network operators to other customers. The analysis indicates that, in the datasets used, A/C systems have most likely resulted in significant bill increases for customers who don’t have them. In contrast, PV systems have most likely had a minimal financial impact on customers who do not have them. While these analyses were undertaken using Australian data, the method is applicable to most countries with modifications to suit the local regulatory environment.

Game-Theoretic Market-Driven Smart Home Scheduling Considering Energy Balancing

In a smart community infrastructure that consists of multiple smart homes, smart controllers schedule various home appliances to balance energy consumption and reduce electricity bills of customers. In this paper, the impact of the smart home scheduling to the electricity market is analyzed with a new smart-home-aware bi-level market model. In this model, the customers schedule home appliances for bill reduction at the community level, whereas aggregators minimize the energy purchasing expense from utilities at the market level, both of which consider the smart home scheduling impacts. A game-theoretic algorithm is proposed to solve this formulation that handles the bidirectional influence between both levels. Comparing with the electricity market without smart home scheduling, our proposed infrastructure balances the energy load through reducing the peak-to-average ratio by up to 35.9%, whereas the average customer bill is reduced by up to 34.3%.

Techno-economic feasibility analysis of solar photovoltaic power generation for buildings

The Building Added PV (BAPV) plays an important role for developing green buildings. This work conducts a techno-economic feasibility study of BAPV for commercial and residential building hybrid energy systems (HES). A component sizing model based on the optimal power dispatch simulations with the objective of minimum cost of energy (COE) is used to determine the component sizes of HES. The techno-economic performances of two HES composed of BAPV and batteries for residential and commercial buildings are investigated. The results show that the use of BAPV in the commercial building HES can reduce the electricity bill for customers owing to the government subsidies on PV as well as due to the similar characteristics of the load profile as to the solar radiation profile. However, due to temporal dislocation between the load and solar radiation patterns, the use of PV in the residential building HES may significantly increase the initial capital cost and replacement cost of battery, resulting in the COE of the residential building HES with BAPV even higher than the residential electricity price. The techno-economic performances of battery (e.g., the lifetime and capital cost) have more effect on the COE of the residential building HES than that of PV.

A New Framework of Demand Response for Household Customers Based on Advanced Metering Infrastructure Under Smart Grids

This article presents a new approach to the energy efficiency of households based on smart grid technologies applied to the existing power grid. The idea is to make the electrical appliances capable of actively responding to the condition of the upstream grid, e.g., the electricity price, which, under market environments, is passed to end-use customers through retailers or load aggregators. This both saves on the electricity bill for customers and improves the energy efficiency of the system as a whole. The key element is advanced metering infrastructure, which performs two main functions: (1) computing the so-called control policy for electrical appliances with anticipated data and (2) real-time control based on the above control policy and actual measurements. By stochastic optimization, the proposed scheme is capable of monitoring the uncertainty related to the electricity consumption in households, e.g., temperature and/or time and quantity demanded, etc. In addition, with the physical model taken into account, the problem can be applied to various appliances, including what were considered critical loads. The proposed scheme was tested in heat, ventilation, and air-conditioning systems; the simulation result showed the effectiveness in comparison with the traditional (on/off) scheme based on thermostats.

Smart Home Activities: A Literature Review

The increasing interest in smart home technologies has created a need for a comprehensive literature survey. This article reviews the goals of a smart home energy management system, along with related definitions, applications, and information about the manufacturing of its components. The challenges associated with smart home energy management systems and possible solutions are examined, and the energy factors that contribute to a customer's electricity bill are discussed. A number of price schemes and the load models needed for solving related scheduling optimization problems are also presented, including a review of the literature related to energy management system scheduling with respect to its control, automation, and communication.

Strategic charging method for plugged in hybrid electric vehicles in smart grids; a game theoretic approach

Implementation of various incentive-based and time-based load management strategies has great potential to decrease peak load growth and customer electricity bill cost. In recent years, developments in Plug in Hybrid Electric Vehicles (PHEVs) have provided various environmental and economic advantages. However, high penetration of electric vehicles in to the grid may cause high peak loads at different times of the days. Using advanced metering and automatic chargers makes it possible to optimize the charging cost, and release generation capacities to provide sustainable electricity supply. Using an appropriate encouraging program is a simple way for vehicle owners to manage their energy consumption and shift the time of charging to proper time of the day; and therefore, to reduce their electricity bill. With these objectives, this paper proposes a new practical PHEVs’ charging scheduling programs aiming at optimizing customers charging cost by considering the generation capacity limitation and dynamic electricity price in different time slots of a day. Using a stochastic model for start time of charging and the duration of it in the proposed optimization algorithm, make this method a practical tool for modeling the vehicle owners’ charging behavior with the purpose of peak load shaving.

Battery energy storage system for peak shaving and voltage unbalance mitigation

Over the last decade, the battery energy storage system (BESS) has become one of the important components in smart grid for enhancing power system performance and reliability. This paper presents a strategy to shave the peak demand and mitigate the voltage unbalance of the electrical networks using a BESS. The BESS is developed to reduce the peak demand and consequently the electricity bill of customers. With the foreseeable large use of BESS, the stress of utility companies can be reduced during high peak power demands. BESS is also equipped with the ability to mitigate voltage unbalance of the network. This indirectly improves the efficiency which in turn, prolongs the life span of three phase machines. The proposed strategy to control BESS has been developed by using the LabVIEW TM graphical programming software. An experimental test bed has been setup at the Universiti Tunku Abdul Rahman (UTAR) campus to evaluate the performance of the system. The experimental results show that the BESS can effectively restrict the power demand from exceeding the pre-determined value and suppress the voltage unbalance factor within the recommended value.