Inclining Block Rate Research Articles

An Innovative Heuristic Algorithm for IoT-Enabled Smart Homes for Developing Countries

Over the past few years, active research on algorithm development for the optimal operations of home energy management systems (HEMSs) has been performed. The objective is to compute optimized schedules for shiftable home appliances. This is based on the demand response (DR) synergized with renewable energy sources and energy storage system optimal dispatch (DRSREOD). An improved algorithm for a DRSREOD-based HEMS is proposed in this paper. This heuristic-based algorithm considers DR, photovoltaic availability, the state of charge and charge/discharge rates of the storage battery and the sharing-based parallel operation of more than one power source to supply the required load. The HEMS problem has been solved to minimize the cost of energy ( $CE$ ) and time-based discomfort ( $TBD$ ) with conflicting tradeoffs. The mixed scheduling of appliances (delayed scheduling for some appliances and advanced scheduling for others) is introduced to improve the $CE$ and $TBD$ performance parameters. An inclining block rate scheme is also incorporated to reduce the peak load. A set of optimized tradeoffs between $CE$ and $TBD$ has been computed to address multi-objectivity using a multi-objective genetic algorithm (MOGA) with Pareto optimization (PO) to perform the tradeoff analysis and to enable consumers to select the most feasible solution. Due to the rapid increase in demand for electricity, developing countries are facing large-scale load shedding (LS). An innovative algorithm is also proposed for the optimal sizing of a dispatchable generator (DG) that can supply the DRSREOD-based HEMS during LS hours to ensure an uninterrupted supply of power. The proposed MOGA/PO-based algorithm enables consumers to select a DG of the optimal size from among a number of optimal choices based on tradeoffs between the DG size, $CE$ , and $TBD$ .

Shaping aggregated load profiles based on optimized local scheduling of home appliances

We present a new method to control an aggregated electric load profile by exploiting the flexibilities provided by residential homes. The method is based on a common energy price combined with inclining block rates, broadcasted to all households allowing them to minimize their energy provisioning cost. The distributed home energy management systems receive the price signal and use mixed integer linear programming for optimal scheduling of load, storage, and generation devices. The method provides excellent scalability as well as autonomy for home owners and avoids load synchronization effects. As proof of concept, an optimization algorithm for determining a day-ahead price is applied in two case studies. An excellent conformance between a given reference load profile and the resulting aggregated load profile of all households is demonstrated.

Optimal household appliance scheduling considering consumer's electricity bill target

This paper presents an optimization algorithm that schedules the operations of household appliances to minimize the monthly electricity bill and maintain comfort for electricity consumers through the interaction between a home energy management system (HEMS) and consumers. In comparison with existing HEMS optimization algorithms over a single day time horizon without considering the monthly electricity bill, the novelty of the proposed method is that the monthly bill target preferred by the consumer is achieved through the optimal operation of appliances over a multi-day time horizon. A key part of the proposed approach is to relax the multi-day optimization problem by adding a penalty on the range of consumer preferred indoor temperature related to operation of the air conditioner, consequently leading to calculation of the desired monthly electricity bill. Simulation studies are illustrated with a single household and four households equipped with smart home appliances in time of use (TOU) and inclining block rate (IBR) pricing tariffs. The results demonstrate that the proposed method enables the total electricity cost and cumulative electricity consumption to successfully converge to each consumer’s target. Furthermore, the impacts of the penalty variable for the relaxed consumer thermal condition and the energy trade between households on the performance of the proposed algorithm are investigated.

Optimal Industrial Load Control in Smart Grid

In this paper, we investigate optimal load control in industrial sector, which involves several new and distinct research problems. For example, while most residential appliances operate independently, industrial units are highly interdependent and must follow certain operational sequences. Also, unlike residential appliances, the operation of industrial units may span across multiple days and involve multiple batch cycles. Furthermore, in industries with process control, energy management is often coupled with material flow management. The design in this paper is comprehensive and addresses industrial load control under various smart electricity pricing scenarios, including day-ahead pricing, time-of-use pricing, peak pricing (PP), inclining block rates, and critical PP. The use of behind-the-meter renewable generator and energy storage is also considered. The formulated optimization problem is a tractable mixed-integer linear program. Different case studies are presented based on a practical energy-extensive steel mill industry model.

Demand side management in developing nations: A mitigating tool for energy imbalance and peak load management

Demand Side Management, an integral feature of smart grid, exhibits high potential in load balancing and load shifting for efficient management of the power grids. Strategically designed DSM mechanisms can help mitigate load shedding and facilitate peak load management of grids. In India, only flat rate and inclining block rate tariff mechanism exist for residential customers. With the onset of 14 smart grid pilot projects in India, there is a need to address the realization of potential underlying various features of a smart grid. As India is a price sensitive market, time of use tariffs and other demand response programs are expected to potentially affect the consumer behavior in power consumption. In this paper, the usage pattern of residential customers, collected from a pilot project in India is analyzed for scaling the potential of DSM strategies. Simulations have been carried out for a price based mechanism as well as an incentive based mechanism to evaluate the underlying potential of DSM exclusively for residential consumers. The results of the simulation indicate that there is a definite scope for load balancing as well as financial savings.

True real time pricing and combined power scheduling of electric appliances in residential energy management system

This paper proposed a new smart home community architecture in power system, in which community controller will acts as a virtual power distribution company. The traditional real time pricing schemes may not be effectively implemented in terms of reduction of power peak to average ratio over the large number of end consumers. To overcome this problem, a true real time pricing between community controller and community end users is developed based on real time pricing and inclining block rates. The proposed pricing scheme implemented in the community is charged at the end of a day according to the combined load of the community. To schedule the electric appliances in a combined way, we have developed a power scheduling algorithm as well. The simulation results have revealed that by applying anticipated technique of pricing scheme in group of households, the consumption cost of end consumers decreases and the overall power peak to average ratio reduces as well which will be beneficial for the utilities.

Stochastic Modeling for the Next Day Domestic Demand Response Applications

Demand response (DR) refers to the consumers' activities for changing the load profile with the purpose of lowering cost, improving power quality or reliability of power system. Enhancement in participation of the DR is widely recognized as a profit-making pattern in distribution systems for both residential units (to increase their benefits) and distribution companies (DISCO) (to reduce their peak demand and costs). The target of this research is concentrated on proposing a new strategy for optimal scheduling of flexible loads for the next day. Then, the day ahead pricing (DAP) is modeled using the inclining block rates (IBR), assumed for retail electricity markets, to investigate the efficiency of the proposed strategy. At the same time, the appliances stochastic time of use (ASTOU) are taken into account in residential units for non-controllable part of the load during a day stochastically. Among five various copulas, the Gaussian copula (GC) function shows the best performance in modeling and estimation of non-controllable load consumption. Finally, simulations, performed with the GAMS, illustrate the effectiveness of the suggested approach which is formulated as a stochastic nonlinear programming (NLP) modeled by the GC. Notice that copulas use samples of real data gathered from residential units.

California Told to “Manage Drought With One Hand Behind Your Back!”

Setting water rates is tricky business, and as a result there is a lot of focus ‐ including legal focus ‐ on it. A recent court decision on water rates in California has, for good reason, captured the attention of the nation. For the past few years, a court case in California (Capistrano Taxpayers Ass'n v. City of San Juan Capistrano) has seemingly raised questions about the level of precision needed to properly set California's water rates ‐ especially inclining block rates (which are often also referred to as tiered rates or increasing block rates). It was not that long ago, before meters were the norm, that the precision of water rates relied on factors such as the number of water fixtures in the home, students in the school, or bar stools at the tavern. Today there are myriad more‐accurate designs that use a customer's actual consumption as the basis to assess rate. These metered designs also improve fairness and equity and simultaneously support utility, public, and environmental policies. The California rate case, however, takes the required level of precision to a new level.

Impact of dynamic energy pricing schemes on a novel multi-user home energy management system

Energy management systems can play an important role in residential energy usage because of recent rapid progress in home appliance technology coupled with rising populations. Home energy management systems (HEMS) technology can provide a smart and efficient way of optimizing energy usage in residential buildings. In addition, a HEMS can help tackle three major issues facing society. The first is reducing greenhouse gas emissions, which are a major driver of climate change. Secondly, energy usage must be minimized in response to increasing energy prices and demand. Finally, energy wastage depletes non-renewable energy resources. This paper presents a HEMS algorithm that monitors and controls household appliances based on a combination of energy pricing models including time of use (TOU), real time pricing (RTP), inclining block rate (IBR) and multiple inhabitants sharing a home and its appliances. This novel algorithm helps to manage and schedule usage by prioritizing multiple uses with preferred usage patterns. Two different scenarios will be implemented to develop and test the influence of a multiple users and load priority (MULP) on reducing energy consumption, energy cost and greenhouse gas emissions. In the first scenario, TOU pricing and different demand limits are used, while the second scenario focuses on the RTP combined with IBR pricing. Simulation results show that the combination of the MULP algorithm and the RTP pricing model with IBR pricing leads to significant reductions in user payments and total energy consumption.

Stochastic modeling for scheduling the charging demand of EV in distribution systems using copulas

Charging demand of electric vehicles (EV) has potentially a significant influence on the power grid. If this charging demand coincides mainly with the peak demand of the power grid, then additional active power has to be supplied to fulfill load management purposes. Thus, it is necessary to estimate and schedule the charging demand of the EV in order to lower the peak demand. Various estimation techniques are available such as Gaussian mixture model and copula. This paper uses copula for data estimation because copula imposes no restriction on the marginal distributions of the available data. Meanwhile, uncertain estimated data requires error elimination. Clayton copula is selected for flexile part of load profile, and Gaussian copula for non-controllable part of the load profile based on the two consecutive days (TCD) classification. Hence, the created scenarios were applied to an optimization problem that flattens the load profile as much as possible using general algebraic modeling system (GAMS). Then, this research concentrates on two new semi-automatically proposals concerned with the day-ahead charging demand response (DR) strategies. These strategies contribute to removing the estimation errors created due to the uncertainties. In order to examine the efficiency of the proposed strategies, the day-ahead pricing (DAP) with inclining block rates (IBR) model is assumed for retail electricity markets.

Manufacturing Scheduling of Collaborative Factories for Energy Cost Reduction

Increasing energy cost and environmental concerns have led to research interests in energy-efficient manufacturing scheduling. Current efforts on optimizing manufacturing schedules under fixed or time- varying electricity prices for reduced energy cost, peak demand, and carbon footprint are largely focusing on a single facility or shop floor. In this paper, scheduling of multiple factories under two time-varying pricing schemes, i.e., time-of-use (TOU) tariff and combined TOU with two-level inclining block rates (IBR) are investigated using mixed integer programming (MIP). Under TOU, the factories do not have incentives to collaborate and each factory optimizes its own schedule for minimal electricity cost independently. However, this leads to the shift of grid peak load from one time period to another. In this paper, TOU-IBR is proposed to address the peak shifting issue. A system consisting of three factories that operate flow shops is used to demonstrate this approach. Two optimization problems will be studied: each factory optimizes manufacturing schedule to minimize its own electricity cost (1) under TOU pricing; and (2) under hybrid TOU-IBR pricing. The result shows that: (1) under TOU pricing, the electricity cost of each factory has been minimized, while the total electricity consumption at the 2nd hour is 6.25% beyond the threshold; and (2) under hybrid TOU-IBR pricing, factories collaborate with each other to achieve energy cost saving, and meanwhile the power consumption at each hour is not over the threshold.

Investigating the Impacts of Different Price-Based Demand Response Programs on Home Load Management

Application of residential demand response (DR) programs are currently realized up to a limited extent due to customers' difficulty in manually responding to the time-differentiated prices. As a solution, this paper proposes an automatic home load management (HLM) framework to achieve the household minimum payment as well as meet the operational constraints to provide customer's comfort. The projected HLM method controls on/off statuses of responsive appliances and the charging/discharging periods of plug-in hybrid electric vehicle (PHEV) and battery storage at home. This paper also studies the impacts of different time-varying tariffs, i.e., time of use (TOU), real time pricing (RTP), and inclining block rate (IBR), on the home load management (HLM). The study is effectuated in a smart home with electrical appliances, a PHEV, and a storage system. The simulation results are presented to demonstrate the effectiveness of the proposed HLM program. Peak of household load demand along with the customer payment costs are reported as the consequence of applying different pricings models in HLM.

Are Residential Customers Price-Responsive to an Inclining Block Rate? Evidence from British Columbia

Are residential customers price-responsive to an inclining block rate? Using BC Hydro's bimonthly billing data, the authors document statistically significant price elasticity estimates to provide BC Hydro with the support for using a residential inclining block rate (RIB) as a tool for energy conservation.

An Optimal Power Scheduling Method for Demand Response in Home Energy Management System

With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

An Optimal Power Scheduling Method Applied in Home Energy Management System Based on Demand Response

In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way possible. In our research, to avoid the high peak-to-average ratio (PAR) of power, we combine the real-time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.

Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid

In this paper, we propose a novel optimization-based real-time residential load management algorithm that takes into account load uncertainty in order to minimize the energy payment for each user. Unlike most existing demand side management algorithms that assume perfect knowledge of users' energy needs, our design only requires knowing some statistical estimates of the future load demand. Moreover, we consider real-time pricing combined with inclining block rate tariffs. In our problem formulation, we take into account different types of constraints on the operation of different appliances such as must-run appliances, controllable appliances that are interruptible, and controllable appliances that are not interruptible. Our design is multi-stage. As the demand information of the appliances is gradually revealed over time, the operation schedule of controllable appliances is updated accordingly. Simulation results confirm that the proposed energy consumption scheduling algorithm can benefit both users, by reducing their energy expenses, and utility companies, by improving the peak-to-average ratio of the aggregate load demand.

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

Piloting the Smart Grid

The transformative power of the smart grid is enormous. It is receiving much consideration from utilities and commissions across North America. Several members of the European Union, China, Japan and other nations are also engaged in the same endeavor. The smart grid has the potential for revolutionizing the way we produce and consume electricity but because it contains so many new elements, its core value proposition remains untested. The unanswered questions include: • What new services will the smart grid provide customers? • Do customers want these new services? • Will they respond by changing their energy use patterns? The answers to these questions will help policymakers in federal and state government determine whether the benefits of the smart grid will cover its costs. It is widely understood that the new services enabled by the smart grid will include different rate designs that encourage curtailment of peak loads and make more efficient use of energy. Examples include dynamic pricing and inclining block rates. These innovative rate designs will be enhanced by various automating technologies such as Energy Orbs, programmable communicating thermostats (PCTs), whole building energy management systems (Auto DR), and in-home displays (IHDs). The smart grid will of course go beyond smart meters and rate design and enable renewable energy resources to be connected to the grid. This will allow optimal use of intermittent resources, such as wind, which often reach their peak generating capacity during off-peak hours. New off-peak loads, such as plug-in hybrid electric vehicles, which reduce overall energy consumption and improve the carbon footprint, will be energized by the smart grid. To address the likely impact of the smart grid on customers, utilities, and society as a whole, it may be necessary to conduct a pilot. When should a pilot be conducted and how should it be conducted? To be useful, a pilot must yield credible results. This requires that the pilot satisfy various validity criteria. These issues form the focus of this paper. We provide examples from several recent pilots that involved dynamic pricing, a key element of the smart gird. The concluding section discusses how a hypothetical company, SMART POWER, should go about designing its own pilot.

DEVELOPING AND ASSESSING A MODEL OF RESIDENTIAL WATER CONSERVATION1

ABSTRACT: Relevant literature was reviewed from which a model of residential water conservation was developed. Four residential conservation program interventions were posited: 1) public education, 2) pricing variables, 3) water use restrictions, and 4) building code requirements. Four exogenous variables affecting residential water use were also posited: 1) temperature, 2) rainfall, 3) household income, and 4) household size. The impacts of these eight variables on residential per capita daily use were assessed by cross sectional and time series analysis. Study results generally supported the porposed model, with less consistent support obtained for pricing variables and conservation beliefs. The paper concludes with the hypothesis that an inclining block rate structure coupled with an informational program designed to inform consumers of their consumption under each block will have a synergistic impact.

Cost‐based water rate design

One method of providing funds for capital improvements to water systems is to set rate structures according to user classifications. Users who provide employment in the community are charged declining block rates, low‐income users can be charged minimum fixed rates, and those who can afford to pay for water for nonessential purposes can be charged by inclining block rates.