Inclining Block Rate Research Articles

Optimal household appliances scheduling for smart energy management considering inclining block rate tariff and net-metering system

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

An innovative method for building electricity energy management in smart homes based on electric vehicle energy capacity

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

Development of two stage optimization-based demand response technique for smart homes under real time pricing

Residential load management deals with two major objectives viz. minimizing the cost of monthly electricity bill and peak demand of power consumption. Both objectives can be achieved by effective operational scheduling of smart home appliances. These two objectives are conflicting in nature because rescheduling of appliances in order to minimize one objective may result in the rise of another. To achieve both objectives concurrently, an algorithm is suggested in this paper based on artificial intelligent techniques like cuckoo search, hybrid GA-PSO, and adaptive cuckoo search. The proposed algorithm is tested successfully on seven households of different monthly power consumption and real data of dynamic pricing options for electricity applicable in two utilities. To reduce the risk associated with real-time price, a cost function based on inclining block rates (IBR) is also suggested for both the utilities. A novel approach to find the threshold limit of hourly power consumption is also suggested in this paper. The proposed algorithm solves the optimization problem in two stages and validates its performance by successfully achieving both objectives simultaneously.

An Integrated Energy Control System to Provide Optimum Demand Side Management of a Grid-Interactive Microgrid

The use of renewables can make it more challenging to maintain a balance between supply and demand in microgrids due to their variable generation profiles. To address this issue, microgrids can be designed to be grid-interactive or to include energy storage units, or both. Additionally, demand-side management (DSM) strategies can be implemented to facilitate control during peak periods. This paper presents a control system for a grid-interactive microgrid with photovoltaic (PV) panels and energy storage units. The proposed system uses a fuzzy-based algorithm to control the energy storage units and provides DSM through the use of a hybrid daily pricing model that combines multi-time rate and inclining block rate pricing methods. To optimize the global maximum power point of the PV panels under partial shading conditions, the microgrid model is tested with several metaheuristic optimization algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), and dragonfly algorithm (DA). A hybrid algorithm that combines PSO and DA is also proposed. The resulting integrated control system includes both demand-side and grid-side control operations.

Dispatch for a Continuous-Time Microgrid Based on a Modified Differential Evolution Algorithm

The carbon trading mechanism is proposed to remit global warming and it can be considered in a microgrid. There is a lack of continuous-time methods in a microgrid, so a continuous-time model is proposed and solved by differential evolution (DE) in this work. This research aims to create effective methods to obtain some useful results in a microgrid. Batteries, microturbines, and the exchange with the main grid are considered. Considering carbon trading, the objective function is the sum of a quadratic function and an absolute value function. In addition, a composite electricity price model has been put forward to conclude the common kinds of electricity prices. DE is utilized to solve the constrained optimization problems (COPs) proposed in this paper. A modified DE is raised in this work, which uses multiple mutation and selection strategies. In the case study, the proposed algorithm is compared with the other seven algorithms and the outperforming one is selected to compare two different types of electricity prices. The results show the proposed algorithm performs best. The Wilcoxon Signed Rank Test is also used to verify its significant superiority. The other result is that time-of-use pricing (ToUP) is economic in the off-peak period while inclining block rates (IBRs) are economic in the peak and shoulder periods. The composite electricity price model can be applied in social production and life. In addition, the proposed algorithm puts forward a new variety of DE and enriches the theory of DE.

Ten lessons in rate design: A meditation

This paper presents ten lessons that I learned in designing rates for electricity over four decades. It is based on designing, reviewing and evaluating rates in multiple jurisdictions around the globe. The rates I helped designed spanned a wide cross section including inclining block rates, time-of-use rates and dynamic pricing rates. The paper reflects my experience as an expert witness in nearly seventy rate cases in the US and Canada. It is also based on I learned from my appearances before government agencies, legislative bodies and regulatory bodies on six continents.

Optimal scheduling of electrical and thermal resources and appliances in a smart home under uncertainty

The development of renewable energy sources, using the Combined Heat and Power (CHP), along with controllable appliances, has provided new opportunities for scheduling resources and consumption in smart homes. This paper proposes a framework for the optimal scheduling of electrical appliances and sources of electricity and heat in a smart home. The smart home is able to purchase electricity through bilateral contracts and spot market. Real-Time Pricing (RTP) and Inclining Block Rate (IBR) tariffs are considered in the spot market. Uncertainties related to production of renewable resources, spot market prices and usage time of inelastic non-shiftable appliances are modeled by scenarios. The presented framework is modeled as a two-stage stochastic optimization problem, with the aim of minimizing the expected cost of the smart home. Then, the problem is formulated in the form of MILP problem. The simulation results showed that the cogeneration of electricity and heat significantly reduced the expected cost of the smart home by 125% compared to the base case study. Meanwhile, the Peak to Average Ratio index (PAR) due to the distribution of consumption along the scheduling horizon has decreased by 35% compared to the base case study.

Scheduling Home Appliances with Integration of Hybrid Energy Sources using Intelligent Algorithms

This paper presents Home Energy Management System(HEMS) for smart homes. This system includes Photovoltaic Sources and Battery Storage Units(BSU)and scheduling Smart Home(SH) appliances with various kinds and types of residential users. In addition, a classification algorithm of energy response programs, which relies on the electricity prices, is introduced for scheduling appliances of single and multiple homes using combined the Time of Use(TOU) and Inclining Block Rate(IBR) to control the demand under the grid station capacity and energy efficacious integration of hybrid power sources in Smart Grid(SG). Four different optimization algorithms used and compared for scheduling appliances. The simulation results show that the proposed algorithm achieves the desired objectives, in which the electricity cost and the Peak Average Ratio(PAR) were lowered, waiting time to the user is minimized, reducing the electricity bill per month/year. In addition, the proposed system increases the stability and reliability of the grid and consumers satisfaction.

A Bilayer Clustered-Priority-Driven Energy Management Model for Inclining Block Rate Tariff Environment

This paper proposes an energy management (EM) model for residences in an inclining block rate (IBR) tariff environment. Given a cluster, i.e., a group of appliances which satisfies certain requirements, the proposed scheme is primarily driven by two layers of clustered priorities. The first layer of clusters comprises various services of the residences and governs it with shareable z1 priorities, and the second layer of clusters binds the major power-consuming appliances under each substation and regulates it with dynamically changing z2 priorities. Integer linear programming is used to solve the multi-criteria decision problem crafted to address both grid-connected and isolated modes of operations. The model is tested on MATLAB using the IEEE 123-bus system with modified Pecan Street Dataset. Various results reveal that the proposed model is effective in providing a reliable power supply to the critical loads in all the scenarios while keeping the price within the preference limit with minimum comfort-loss for end-users, even in presence of non-responsive IBR environment

A refined consumer behavior model for energy systems: Application to the pricing and energy-efficiency problems

Sum-utility maximization is an important problem in the optimization of energy systems. A conventional assumption in addressing this problem is that the utility to be maximized is concave. But for some key applications, such an assumption is not reasonable and does not reflect well the actual behavior of the consumer. To address this issue, the authors pose and address a more general optimization problem, namely by assuming the consumer’s utility to be sigmoidal and in a given class of functions. The considered class of functions is attractive for at least two reasons. First, the classical NP-hardness issue associated with sum-utility maximization is circumvented. Second, the considered class of functions encompasses well-known performance metrics used to analyze problems of pricing and energy-efficiency. This allows one to design a new and optimal inclining block rate (IBR) pricing policy which also has the virtue of flattening the power consumption and reducing the peak power. We also show how to maximize the energy-efficiency using a low-complexity algorithm. When compared to existing policies, simulations fully support the benefit of using the proposed approach.

Water Consumption and Utility Revenues at the Start of a Pandemic: Insights From 11 Utilities.

Key TakeawaysEleven utilities from across the United States were studied to understand the pandemic's effects on water consumption and utility revenues.Most utilities in the study saw an overall increase in water consumption with a rise in residential demand that offset declines in nonresidential demand.Most utilities in the study experienced increased revenues in 2020 compared with previous years, largely due to rate increases, inclining block rates, and an unusually warm summer.Water utilities’ ability to adjust to fluctuating demand and changing revenue patterns is a testament to their sound financial practices.

Intelligent approach for residential load scheduling

Residential load scheduling has been introduced in recent years as a result of implementing demand response programmes in the residential sector. Up to now, studies have focussed on developing algorithms for scheduling the residential loads with the objective of reducing the electricity payment while satisfying the users’ comfort level. To aim at this goal, the user should determine the desired scheduling window for each appliance to initiate the scheduling process. Although these algorithms designed to benefit the end-users, determining the scheduling window for each appliance at the beginning of the scheduling process can be challenging. In this study, an intelligent load scheduling model is proposed to perform the scheduling with the least user intervention. The proposed model predicts the preferred scheduling window for each appliance and initiates the scheduling process. Furthermore, the degradation cost of the battery pack in the vehicle-to-grid mode is considered in the cost function. The model is applied to a case study containing plug-in hybrid electric vehicle and flexible loads such as a washing machine and dishwasher under inclining block rates and hourly pricing tariffs. The results indicate that implementing the model in a residential building can decrease the electricity payment while performing the residential load scheduling with the least user intervention.

Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries

Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

Optimal scheduling of resources and appliances in smart homes under uncertainties considering participation in spot and contractual markets

In this paper, a new framework is proposed for optimal energy resource management in a smart home comprising of controllable and non-controllable appliances and local renewable resources. Here, the smart home can procure the required energy through both the spot and contractual markets under Real-Time Pricing (RTP) mechanism. Uncertainties in the production of renewable resources, spot market price and non-controllable appliances, modeled by sets of scenarios, are considered. Controllable appliances are classified into two groups of continuous and interruptible, while the non-controllable appliances are classified into elastic and inelastic groups. Both RTP and Inclining Block Rate (IBR) tariffs are used to prevent the consumption in certain hours in addition to a reflection of spot market fluctuations. First-order Markov chain and Multi-Layer Perceptron (MLP) neural networks are used to predict the production of renewable resources. The optimization problem is designed to minimize the consumer’s expected net cost in the form of a two-stage stochastic problem and is formulated as a MILP problem. The model is applied to smart home to illustrate the impact of the proposed model. Simulation results show the positive impact of the proposed scheduling method on reducing consumer costs and network Peak to Average Ratio (PAR).

Energy management of smart homes equipped with energy storage systems considering the PAR index based on real-time pricing

The construction of smart homes under the infrastructure of smart grids (or micro grids) is currently one of the most important topics in the field of smart energy systems. Demand-side management confers many benefits on both users and utility companies. This paper proposes a novel scheduling procedure for power consumption in homes equipped with energy storage devices. The proposed optimal power scheduling method can reduce electricity bills and improve peak-to-average ratio (PAR) while taking into account the comfort of residents. Real-time pricing combined with an inclining block rate (IBR) tariff is used to prevent high power consumption at times of low cost, and to simultaneously improve the PAR index. Because erroneous determination of the state of the charge of storage systems can have deleterious effects on PAR, a revised formulation is therefore applied to attain the best solution of the problem. Home devices and batteries are modelled in a practical and efficient manner. The proposed multi-objective optimization problem is solved with non-dominated sorting genetic algorithm (NSGA-II) using MATLAB software. Then fuzzy decision is applied to obtain the most preferred solution.

Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources

Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort.

An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

A Robust Optimization Approach for Demand Side Scheduling Considering Uncertainty of Manually Operated Appliances

Manually operated appliances (MOAs) are manually operated based on users’ real-time demands and their energy consumption is uncertain to other schedulable appliances (SAs). This paper represents energy consumption scheduling of home appliances under the uncertainty of the MOAs as a robust optimization problem, as uncertainty distribution of MOAs is usually unknown and not easily estimated. Among all possible energy consumption cases of the MOAs, the robust approach takes into account the worst case to reduce electricity payment of all home appliances, based on the real-time electricity pricing scheme combined with inclining block rate. Intergeneration projection evolutionary algorithm, which is a nested heuristic algorithm with inner genetic algorithm and outer particle swarm optimization algorithm, is adopted to solve the robust optimization problem. Case studies are based on one day case, and one month case with various combinations of SAs and MOAs. Simulation results illustrate the effectiveness of the proposed approach in reduction of electricity payment compared with the approach without considering the uncertainty of MOAs, and the approach considering MOAs with fixed pattern.