Peak Demand Reduction Research Articles

Optimal Scheduling Strategy for Domestic Electric Water Heaters Based on the Temperature State Priority List

With the rapid growth of thermostatically controlled loads, active power fluctuation and peak demand growth within an autonomous micro-grid become serious problems. This paper tries to suppress power fluctuation and shave peak demand for a micro-grid through optimizing domestic electric water heaters (controllable load). In this paper, domestic electric water heater models are first built to optimize power flow within a single water heater. Subsequently, the Monte Carlo method is proposed to simulate power consumption of a cluster of domestic electric water heaters. After that, the temperature state priority list method is presented to suppress power flow and shave peak demand for a given micro-grid. Optimization results show that the proposed temperature state priority list method can reduce peak demand by 12.5%. However, it has a wider active power fluctuation range and needs a longer reaction time compared with the simplified temperature state priority list method. In addition, the optimization results show that by increasing the number of controllable loads participating in load scheduling, active power fluctuation can be reduced and the maximum active power of the given micro-grid can be cut. However, to achieve this, about 1.2% of extra electrical energy needs to be generated by the external grid.

Using Thermal Inertia of Buildings with Phase Change Material for Demand Response

Abstract: In recent years, demand response programs have proved useful in managing peak demand and meeting sustainability goals, enabling an efficient use of the smart grid. Heating, ventilation, and air conditioning (HVAC) loads in buildings constitute a large proportion of the total energy consumption of households, and accordingly, a flexible and efficient operation of these devices can aid power utilities in meeting load management objectives while reducing consumer’s electricity bills. With the emergence of promising new technologies, such as phase change materials (PCM), buildings can serve as a virtual thermal energy storage, which improves energy efficiency and also allows occupants to offer grid services like peak demand reduction. The objective of this paper is to establish the effectiveness of PCM as a demand response resource, demonstrating the extent to which it can be used for peak demand reduction. A wide range of scenarios are considered to investigate the impacts of geographical location, PCM melting point, duration of precooling and preheating, setting points of HVAC system, thickness and location of PCM, on the capability of the PCM in reducing or shifting the cooling and heating load. All simulations are performed using the EnergyPlus platform, examining typical residential buildings in five Australian cities: Brisbane, Sydney, Melbourne, Hobart and Perth. The simulation results showed a decrease in the HVAC demand in the buildings with PCM, in all cities, with the highest reductions observed in Hobart and Melbourne. The integration of a 20mm thick PCM in the roof, wall and floor of the building yielded a 21.8% and 16.7% reduction in annual HVAC demand in Hobart and Melbourne respectively, when compared to the building without PCM. However, this is with the assumption that the HVAC system is operating 24 hours a day for a whole year. The PCM-integrated building showed a shift in the HVAC demand in all cities except Perth. A shift by 9 minutes, 3 minutes, 60 minutes and 103 minutes was recorded in the cities of Brisbane, Sydney, Melbourne and Hobart respectively. The simulation results will be used in subsequent research to schedule the HVAC demand using a home energy management system.

A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings

With the advent of the smart grid, demand response (DR) has been implemented in many electric utility control areas to reduce peak demand in buildings during grid stress conditions. However, small- and medium-sized commercial buildings typically do not deploy a building energy management (BEM) system due to high costs of commercially available solutions. Thus, their participation in DR events implies manual control and shutting down major building loads (e.g., air conditioning systems) without considering occupant comfort. With rapid development of Internet of Things (IoT) technologies, some cost-effective IoT-based BEM systems have become available. Based on such systems, this paper presents an algorithm to automatically coordinate the operation of rooftop units (RTUs) in small- and medium-sized commercial buildings, thereby meeting the specified power limit (kW) during a DR event while taking into account occupant comfort. The proposed algorithm has been designed to intelligently learn building thermal properties using coarse-grained indoor temperature data from thermostats, thus avoiding the deployment of sophisticated sensors network. A mixed-integer linear programming model has been utilized to determine an optimal RTU control strategy during a DR event. The peak load shedding performance of the proposed strategy has been tested in an office building in Blacksburg, VA, USA. The experimental result demonstrates that the building could achieve the required peak load reduction and the computation time required by the proposed algorithm is less than 5min. This implies that with the proposed algorithm a building is capable of responding to a DR signal with a short notice, providing valuable demand-side resources for electricity capacity and ancillary markets.

Impact of structural design solutions on the energy and thermal performance of an Australian office building

Concrete is a heavyweight construction material whose high thermal mass could increase the thermal storage capacity of a building envelope and in turn affect indoor thermal comfort. Selecting an appropriate method for concrete construction and form could also affect the total energy performance and thermal comfort of a building, a fact that is often overlooked by structural engineers. This study presents the results of energy simulations of the potential impact that concrete construction forms, in particular two slab types, and structural materials have on the energy consumption of archetypal commercial office buildings in five major Australia cities (Sydney, Melbourne, Canberra, Brisbane and Darwin). This study has three stages: 1) a structural analysis of two slab types (Flat and Waffle slab); 2) the selection of two types of structural concrete (conventional Normal weight concrete and novel Ultra-lightweight concrete); 3) a comparative analysis to quantify the magnitude of the change in predicted annual energy consumption due to changes in the form of construction and the type of structural concrete. The energy simulation results showed that the thermal energy performance of the building was influenced by structural materials and slab types. It is shown that the thermal capacity of the concrete construction forms can be utilized to shift thermal loads, reduce peak demand and reduce operational energy consumption. The selection of an appropriate concrete type was more important in terms of energy performance in the coldest (Melbourne and Canberra) and hottest (Darwin) climate zones of this study.

Economic Analysis and Implementation Strategies for SOFC Systems in Residential Applications

In this study, we installed and evaluated a 1.5 kW Solid Oxide Fuel Cell (SOFC) with Micro-CHP (combined heat and power) system as a Distributed Generation (DG) prime mover in a residential application. Both steady state and dynamic operating conditions were evaluated. With the steady state and dynamic performance characteristics of the SOFC CHP system, and the latest capital cost and performance data garnered from the ENE FARM Program (Japan) and ene. field Program (Europe), we carried out an economic analysis taking into account the daily and seasonal energy production, residential utility rates, fiscal incentives by state, and effective photovoltaic (PV) capacity integration. Various operation strategies that focus on 1) reduced energy use, 2) reduced peak demand, 3) PV integration at the residence, and 4) qualifications for fiscal incentive programs are evaluated. From the customer perspective, this study generates a guideline for the user to make decisions regarding SOFC system capacity, operating strategy and CHP configuration, while providing guidance regarding the breakeven costs of SOFC systems under various scenarios in various regions.

Economic Analysis and Implementation Strategies for SOFC Systems in Residential Applications

In this study, with measured steady state performance characteristics of a 1.5 kW residential Solid Oxide Fuel Cell (SOFC) system we evaluated the installation of residential SOFC systems for a typical hourly energy use for the entire year through all 16 climate zones in California. We carried out an economic analysis taking into account the dynamic daily and seasonal energy consumption for various climate zones and various current residential time-of-use utility rates. Residential SOFC implementation cases in various climate zones that focus upon 1) reduced energy use, 2) reduced peak demand, 3) utility time-of use rate-structure impacts, and 4) CO2 emission reductions are evaluated. From the customer perspective, this study generates a guideline for the user to make decisions depending upon their electric energy usage profile, utility rate structure and carbon credit, we found that breakeven costs are highly dependent upon the utility rate structures with PG&E structures most favorably encouraging SOFC use.

The value of electricity storage to large enterprises: A case study on Lancaster University

Co-locating electricity storage with demand has significant potential to increase consumption of locally-generated electricity, defer infrastructure investments, and contribute to the task of balancing supply and demand on the wider network. In the UK, unlike domestic consumers, large enterprises are already incentivised to reduce peak demand through exposure to time- and demand-dependent network charges. This paper considers the potential of electricity storage to reduce the bills of large enterprises, focusing on Lancaster University as a case study. Through analysis of Lancaster University's recent demand and generation data and current and future charges, it is shown that recent widening of red distribution charge time bands has reduced the value of electricity storage to enterprises, and that in 2015 an enterprise such as Lancaster University could have expected electricity storage to deliver annual savings of around £27 per kWh of storage capacity, by reducing network charges. An analysis of these charges around Great Britain shows that the opportunity for storage to provide savings to enterprises is greatest in the south-west (at least £70/kWh.yr in 2017) and lowest in the north of Scotland (at least £20/kWh.yr). Whether investment in storage provides positive value to enterprises is shown to be strongly dependent upon location.

A Multiagent Minority-Game-Based Demand-Response Management of Smart Buildings Toward Peak Load Reduction

This paper presents a cyber-physical management of smart buildings based on smart-gateway network with distributed and real-time energy data collection and analytics. We consider a building with multiple rooms supplied with one main electricity grid and one additional solar energy grid. Based on smart-gateway network, energy signatures of rooms are first extracted with consideration of uncertainty and further classified as different types of agents. Then, a multiagent minority-game (MG)-based demand-response management is introduced to reduce peak demand on the main electricity grid and also to fairly allocate solar energy on the additional grid. Experiment results show that compared to the traditional static and centralized energy-management system (EMS), and the recent multiagent EMS using price-demand competition, the proposed uncertainty-aware MG-EMS can achieve up to $50\times $ and $145\times $ utilization rate improvements, respectively, regarding to the fairness of solar energy resource allocation. More importantly, the peak load from the main electricity grid is reduced by 38.50% in summer and 15.83% in winter based on benchmarked energy data of building. Lastly, an average 23% uncertainty can be reduced with an according 37% balanced energy allocation improved comparing to the MG-EMS without consideration of uncertainty.

Enabling Distributed Energy Storage by Incentivizing Small Load Shifts

Reducing peak demands and achieving a high penetration of renewable energy sources are important goals in achieving a smarter grid. To reduce peak demand, utilities are introducing variable rate electricity prices to incentivize consumers to manually shift their demand to low-price periods. Consumers may also use energy storage to automatically shift their demand by storing energy during low-price periods for use during high-price periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at large scales. In this article, we present the storage adoption dilemma to capture the problems with incentivizing energy storage using variable rate prices. To address the problem, we propose a simple pricing scheme, called flat-power pricing , which incentivizes consumers to shift small amounts of load to flatten their demand rather than shift as much of their power usage as possible to low-price, off-peak periods. We show that compared to variable rate pricing, flat-power pricing (i) reduces consumers’ upfront capital costs, as it requires significantly less storage capacity per consumer; (ii) increases energy storage’s return on investment, as it mitigates free riding and maintains the incentive to use energy storage at large scales; and (iii) uses aggregate storage capacity within 31% of an optimal centralized approach. In addition, unlike variable rate pricing, we also show that flat-power pricing incentivizes the scheduling of elastic background loads, such as air conditioners and heaters, to reduce peak demand. We evaluate our approach using real smart meter data from 14,000 homes in a small town.

Distributed generation: Residential storage comes at a cost

The combined effect of increased variability of demand due to distributed generation and domestic storage deployment represents a new feature in modern electricity systems. A recent study shows that while storage can reduce peak demand, it could also increase overall consumption and electricity system emissions.

Methodology for Measurement the Energy Efficiency Involving Solar Heating Systems Using Stochastic Modelling

The purpose of the present study is to evaluate gains through measurement and verification methodology adapted from the International Performance Measurement and Verification Protocol, from case studies involving Energy Efficiency Projects in the Goias State, Brazil. This paper also presents the stochastic modelling for the generation of future scenarios of electricity saving resulted by these Energy Efficiency Projects. The model is developed by using the Geometric Brownian Motion Stochastic Process with Mean Reversion associated with the Monte Carlo simulation technique. Results show that the electricity saved from the replacement of electric showers by solar water heating systems in homes of low-income families has great potential to bring financial benefits to such families, and that the reduction in peak demand obtained from this Energy Efficiency Action is advantageous to the Brazilian electrical system. Results contemplate also the future scenarios of electricity saving and a sensitivity analysis in order to verify how values of some parameters influence on the results, once there is no historical data available for obtaining these values.

Water Supply Operation and Scheduling System with Electric Power Demand Response Function

A water supply operation and scheduling system with a power demand response (DR) function was proposed and evaluated in a case study. The system has two operation modes achieved by the DR function, that is, a power smoothing operation mode that seeks to smooth electric power use in ways that cut the basic tariff by cutting or shifting demand peaks and a DR operation mode that earns incentives by reducing demand in response to requests. The benefits of using the system for electric power management were assessed for a medium-sized water supply system. In an operation using both DR and smoothing operation modes at once, the scheduling system succeeded in reducing peak demand during the time period covered by DR requests (1 PM to 4 PM) by approximately 60%, from 1372 kW under existing practices to 545 kW. Moreover, peak daily demand was also reduced by approximately 16%, from 1772 kW to 1492 kW.

An Intelligent Load Management System With Renewable Energy Integration for Smart Homes

Demand side management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via home energy management systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, evolutionary algorithms-based (binary particle swarm optimization, genetic algorithm, and cuckoo search) DSM model for scheduling the appliances of residential users is presented. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.

Evaluation of ice thermal energy storage (ITES) for commercial buildings in cities in Brazil

High temperatures and the intensive use of air conditioning are considered the main drivers of the increase in Brazil’s electricity demand in the summer, which has been causing major power cuts due to insufficient supply to meet peak demand. This paper analyses the techno-economic aspects of Ice Thermal Energy Storage (ITES) in Brazil, as a technology that can be used to shift building cooling load to off-peak time. ITES is assessed in five cities and five archetypes of commercial buildings, using two different operation strategies and two types of chillers, for new installations and retrofits. Results have shown that ITES can effectively shape peak demand in commercial buildings, generating economic gains for building owners but also losses in some cases. Although the climate plays an important role, the electricity tariffs vary significantly from city to city and this determined the economic feasibility of ITES. The technology’s potential to shape demand in Brazil’s commercial sector can encourage policy makers, regulators, grid operators and utilities in considering ITES as an option to reduce peak demand, increase security of supply and support the integration of intermittent renewable generation.

Experimental determination of demand side management potential of wet appliances in the Netherlands

Abstract The potential of demand side management (DSM) of wet appliances (washing machine, dishwasher and tumble dryer) in households with photovoltaic (PV) systems is studied experimentally focusing on evening peak demand reduction and increase in PV self-consumption. In a sample of 100 Dutch households the electricity demand is monitored for one year at high time resolution. It is found that the dishwasher has the best DSM potential, with a median average peak reduction potential up to 35 W per household on average for all appliances, while PV self-consumption for the median household can be increased with 68 kWh/year per appliance. The tumble dryer and washing machine’s potentials are 30 W and 25 W per household, respectively, for the mean average peak reduction, and 38 kWh and 28 kWh, respectively, per household per year for increase in PV self-consumption per appliance. Further, the median peak reduction potential of a single group of wet appliances ranges from 1% to 5% of the power demand in peak hours, while combined they could add up to 10% of the total peak. The median increase in PV self-consumption is in the range between 1.5% and 4% for separate appliances, while the median total could provide an increase up to 129 kWh per household per year if a household possesses and utilizes the potential of all appliances, which is about 6% of the annual household demand. In conclusion, DSM potential for wet appliances is limited.

Reduction of peak demand in micro smart grid by means of elastic model of power management

Reduction of peak demand in micro smart grid by means of elastic model of power management

Smart Emergency Generator Monitoring System through IoT using IEC 61850

Objectives: The energy consumption increased day by day. To reduce the peak power demand, there are many studies of Distributed Energy Resource like emergency generator and renewable energy. Methods/Statistical Analysis: In Korea, emergency generator capacity is 21GW. If some of emergency generators utilize, it efficiently reduces peak demand. However, the emergency generator is spread all over the country, it need to monitoring and integrating technology by remote site. Findings: IEC 61850 is standard of integrating automation substation and IEC 61850-7-420 describes LNs of Distributed Energy Resources. So, we check up the selection and installation regulation of emergency generator by KOSHA CODE and sort the check list like temperature, pressure in system. So, we installed the various sensors (oil temperature, coolant temperature, oil pressure, fuel level). The sensor value output is 4 to 20mA analog data. The controller coverts analog to digital data with Modbus protocol and IEC 61850 gateway creates IED mapping Modbus to IEC 61850. The data transfer VPN with TCP/IP protocol and it can monitor the remote site. Improvements/Applications: The proposed system can supply smart service to users with IoT technology and they can predict generator fault and reduce the accident. Furthermore, this system can adapt other Distributed Energy Resources.

Effects of Voluntary Time-of-Use Pricing on Summer Electricity Usage of Business Customers

Economic inefficiency can be caused by time-invariant retail electricity prices because they do not reflect variations in the cost of providing electricity during the day. Time-of-use (TOU) pricing—higher electricity prices during peak hours and lower electricity prices during off-peak hours—is by far the most common way to achieve more efficient levels of electricity consumption through reducing peak demand. The empirical evidence of the effectiveness of TOU pricing is sparse in the commercial and industrial sectors and there is no consensus in the literature on the statistical significance and magnitude of the effects. Applying a quasi-experimental design, this study evaluates an ongoing experiment of voluntary business TOU pricing plan by a major utility company in the Phoenix metropolitan area. Using the nearest-neighbor matching method, we identify control customers for the voluntary participants of the business TOU pricing. From difference-in-differences analysis, we find a statistically significant reduction in peak-hour electricity demand in response to the TOU pricing. We also find that there is no conservation effect, meaning that the total level of electricity consumption does not change under the TOU pricing.

Retrofitting building fire service water tanks as chilled water storage for power demand limiting

Peak demand cost usually contributes a large proportion of the total electricity bills in buildings. Using existing building facilities for power demand limiting has been verified as effective measures to reduce monthly peak demands and associated costs. Fire service water tanks exist in most commercial buildings. This paper presents a comprehensive study on how to effectively retrofit existing building fire service water tanks as chilled water storage for power demand limiting. Important technical and economic factors that may affect the implementation of the proposed retrofitting are addressed. Two retrofitting schemes, i.e. a small ΔT (storage temperature difference) scheme and a large ΔT scheme are proposed for integrating the chilled water storage system into an existing all-air system and an existing air-water air conditioning system, respectively. Two optimal demand limiting control strategies, i.e. time-based control and demand-based control, are proposed for maximizing the monthly peak demand reduction of buildings with regular and variable peak occurring time, respectively. The cost-effectiveness of different retrofitting schemes in three real buildings in Hong Kong is analysed. Results show that substantial cost savings can be achieved with short payback periods (0.7–2.6 years) for the retrofits in these three buildings. Practical application: This paper presents a techno-economic analysis on retrofitting existing building fire service water tanks as chilled water storage for power demand limiting and operational cost saving. The proposed retrofitting schemes and demand limiting control strategies enable chilled water storage systems to be readily applied to most existing buildings. Building owners can benefit from the peak demand cost saving as the monthly peak demand can be significantly reduced by using chilled water storage. The extra costs involved in tank retrofits and system integrations can be paid back within three years.

Modeling of a portable electric spa: Model development, experimental validation and application to winter demand response

This paper presents a new model for portable electric spas, also referred to as spas or hot tubs. The developed model is implemented in the TRNSYS simulation tool, and is referred to as Type 3252. It uses two thermal nodes to model the water temperature and the air temperature in the cavity below the spa. The model is calibrated and validated using experimental data and the results show that the thermal behavior of the spa is adequately simulated, both for quasi steady-state performance and transient performance. The validated model is used to assess demand reduction strategies in a cold climate context, with the aim of reducing peak demand during critical days, both for the morning (from 6:00 to 9:00) and the evening (from 16:00 to 20:00) peak periods.