Electricity Peak Load Research Articles

제습증발냉각시스템의 PV/T 적용을 통한 에너지 소비량 및 이산화탄소 배출량 저감효과 분석

The main thrust of this paper is to estimate the energy savings and CO2 reduction potentials acquired by hybrid photovoltaic/thermal(PV/T) system in to the established liquid desiccant-assisted evaporative cooling system. The PV/T system provides the electric power and hot water simultaneously, which are required to operate the liquid desiccant unit in the established system. The required capacity of the PV/T system can be determined for the peak electric load or peak thermal load of the liquid desiccant-assisted evaporative cooling system. Consequently, energy simulations were performed for the two different PV/T integrated system alternatives; eletric load-follow and thermal load-follow operation. The result shows that the PV/T system sized for the electric load may provide more than 20% of primary energy savings and CO2 emissions reductions compared with the PV/T system sized for the peak thermal load. Also it has economical, energetic and environmental benefits in payback time.

Energy system impacts from heat and transport electrification

Electrifying the energy system and powering it by low carbon electricity is one of the key decarbonisation pathways of the energy system. This study examines annual electricity and gas consumption in a high electrification scenario in Great Britain (GB) and the implications for electricity generation and transmission infrastructure using a suite of soft-linked models. High electrification of heating and transport services, which are two major fossil fuel consumers in GB, increases annual electricity consumption and peak electricity load by 35% and 93%, respectively, by 2050 while reducing overall annual energy consumption compared to a reference case. Meeting this high electricity consumption with a supply strategy that is dependent on offshore wind could more than double the supply-side investments required compared to a reference case, if demand-side measures are not available. High electrification would also impact existing gas and oil energy infrastructure by reducing consumption of these fuels. It was found that uncertainties in socio-economic growth can amplify these implications and therefore need serious consideration by analysts and policymakers involved in designing energy transition strategies. A case study and discussion demonstrate that smart-grid aided demand-side management has the potential to minimise electricity peak load and infrastructure requirements from high electrification.

Stochastic scenario‐based model and investigating size of energy storages for PEM‐fuel cell unit commitment of micro‐grid considering profitable strategies

This paper presents a unit commitment formulation for micro-grid that includes a significant number of grid parallel Proton Exchange Membrane-Fuel Cell Power Plants (PEM-FCPPs) with ramping rate and minimum up/down time constraints. The aim of this problem is to determine the optimum size of energy storage like battery storages and use the efficient hydrogen and thermal energy storages and to schedule the committed units' output power while satisfying practical constraints and electrical/thermal load demand over one day with 15 min time step. In order to best use of multiple PEM-FCPPs, hydrogen storage management is carried out. Also, since the electrical and heat load demand are not synchronised, it could be useful to store the extra heat of PEM-FCPPs in the peak electrical load in order to satisfy delayed heat demands. Due to uncertainty nature of electrical/thermal load, photovoltaic and wind turbine output power and market price, a two-stage scenario-based stochastic programming model, where the first stage prescribes the here-and-now variables and the second stage determines the optima value of wait-and-see variables under cost minimization is implemented. For solving the problem, a new enhanced cuckoo optimisation algorithm is presented and successfully applied to two typical micro-grids. Quantitative results show its usefulness.

Peak Electric Load Relief in Northern Manhattan

The aphorism “Think globally, act locally,” attributed to René Dubos, reflects the vision that the solution to global environmental problems must begin with efforts within our communities. PlaNYC 2030, the New York City sustainability plan, is the starting point for this study. Results include (a) a case study based on the City College of New York (CCNY) energy audit, in which we model the impacts of green roofs on campus energy demand and (b) a case study of energy use at the neighborhood scale. We find that reducing the urban heat island effect can reduce building cooling requirements, peak electricity loads stress on the local electricity grid and improve urban livability.

Characteristic Analysis of Peak Load in Electricity on Large Scale Hotels Considering the Energy Efficiency

The demand for electricity in Korea has been on the rise due to economic growth, changes in weather conditions, and the widespread use of electric devices. The excessive demand for electricity that used to occur only during the summer now occurs during the winter as well. As a result, it is now necessary to manage the electricity load of buildings in a stable way. The study is intended to analyze the possibility of reducing the entire electricity load and electricity peak load through an improvement in the energy efficiency of hotels buildings that are typical high energy consuming buildings, and to propose a plan to reduce electricity peak load through a decrease in the entire electricity load. Using the electricity load data of the hotel buildings, the properties of electricity load and the correlations between building energy efficiency rating and electricity peak load were analyzed. In so doing, this study can contribute not only to reducing the peak load of buildings, but also leveling off energy supply and load through a reduction in the nation's largest electricity demand.

Capacity payment impact on gas-fired generation investments under rising renewable feed-in — A real options analysis

We assess the effect of capacity payments on investments in gas-fired power plants in the presence of different degrees of renewable energy technology (RET) penetration. Low variable cost renewables increasingly make investments in gas-fired generation unprofitable. At the same time, growing feed-in from intermittent RETs amplifies fluctuations in power generation, thus entailing the need for flexible buffer capacity—currently mostly gas-fired power plants. A real options approach is applied to evaluate investment decisions and timing of a single investor in gas-fired power generation. We investigate the necessity and effectiveness of capacity payments. Our model incorporates multiple uncertainties and assesses the effect of capacity payments under different degrees of RET penetration. In a numerical study, we implement stochastic processes for peak-load electricity prices and natural gas prices. We find that capacity payments are an effective measure to promote new gas-fired generation projects. Especially in times of high renewable feed-in, capacity payments are required to incentivize peak-load investments.

Stochastic scenario-based model and investigating size of battery energy storage and thermal energy storage for micro-grid

Energy storage systems (ESS) are designed to accumulate energy when production exceeds demand and to make it available at the user’s request. They can help match energy supply and demand, exploit the variable production of renewable energy sources (e.g. solar and wind), increase the overall efficiency of the energy system and reduce CO2 emissions. This paper presents a unit commitment formulation for micro-grid that includes a significant number of grid parallel PEM-Fuel Cell Power Plants (PEM-FCPPs) with ramping rate and minimum up and down time constraints. The aim of this problem is to determine the optimum size of energy storage devices like hydrogen, thermal energy and battery energy storages in order to schedule the committed units’ output power while satisfying practical constraints and electrical/thermal load demand over one day with 15min time step. In order to best use of multiple PEM-FCPPs, hydrogen storage management is carried out. Also, since the electrical and heat load demand are not synchronized, it could be useful to store the extra heat of PEM-FCPPs in the peak electrical load in order to satisfy delayed heat demands. Due to uncertainty nature of electrical/thermal load, photovoltaic and wind turbine output power and market price, a two-stage scenario-based stochastic programming model, where the first stage prescribes the here-and-now variables and the second stage determines the optima value of wait-and-see variables under cost minimization. Quantitative results show the usefulness and viability of the suggested approach.

Feature selection for daily peak load forecasting using a neuro-fuzzy system

Accurate electrical daily peak load forecasting (DPLF) is essential for power system management in order to prevent overloading and grid failure. Fuzzy neural networks have been successfully applied to load forecasting due to their nonlinear mapping and generalized behavior. In this paper, a neuro-fuzzy based DPLF (N-DPLF) model with a feature selection method is proposed for DPLF. The load data is clustered into seven subsets according to the season and day type. For each subset, the four features with the highest salience ranks are selected. After training N-DPLF model, the formed BSWs (bounded sum of weighted fuzzy membership functions) in accordance with the selected features denote characteristics of these features. The N-DPLF model provides explicit BSWs in hyperboxes, instead of the uncertain black box nature of neural network models, so that the selected features can be interpreted by the visually constructed BSWs. The N-DPLF model with a feature selection method shows a mean absolute percentage error (MAPE) of 1.86 % using Korea Power Exchange data over 1-year period.

Research of Electric Vehicles' Discharging Price

Through the vehicle to the network (V2G) control, the objective of the "load shifting" is possible. Reasonable charging and discharging price is the fundamental driving force for EV users to participate in V2G and the analysis of the game process of the grid company and the EV users becomes critical. A bilevel programming problem is established which makes company the largest gains and consumers the minimum cost. Through a control scheme whose purpose is minimizing the sample variance of load, the relationship of annual discharging electricity energy and peak load decrement is obtained. At last the optimal discharging price of power grid company and the corresponding proportion of EV participating in V2G is calculated by chaos algorithm. It provides a reference for company to formulate discharging price.

Peak Load Shifting and Electricity Charges Reduction Realized by Electric Vehicle Storage Virtualization

Electric Vehicle (EV) battery is large capacity, which is equivalent to two days of home power consumption, and cheaper than household battery. Therefore, it is important to utilize as home backup power to reduce home electricity charges. In this paper, we propose a new EV battery demand/response control method, which consists of three items; a new Electric Vehicle (EV) batteries ownership virtualization technique realized by “deposited power concept”, a huge virtual battery pool to enable charge/discharge at any time, and a genetic algorithm to control demand/supply of EV batteries. Center controller named EVNO (Energy Virtual Network Operator) has a huge virtual battery pool which is aggregated by “deposited power” of each EV, and controls demand/supply of each EV by the genetic algorithm. Since EVNO controls the deposited power among their EV batteries, EV users lose ownership of the deposited electric power in their EV batteries. At this time, EV owner does not use the electric power in his EV physically. The computer simulation result shows that the proposed method can reduce electricity charges by average 11%, and can reduce power demand curve by average 13% per day compared to conventional scheme under the real-time pricing (RTP).

Experimental analysis of an unconventional desiccant-based air-conditioning system: the influence of cooling air flow and chiller on the energy and environmental performance

Nowadays, the increasing demand of summer cooling is typically covered by electric chillers, often determining electric peak loads and black-outs. Thus, a wide interest is spreading in small scale natural gas-fired cogenerators driving desiccant-based air-conditioning systems, which represent interesting alternatives to conventional systems based on vapor compression cooling only. In this article, experimental tests performed on an air handling unit (AHU) equipped with a desiccant wheel (DW), coupled to a small scale cogenerator and an electric chiller are described. A new layout of the desiccant-based AHU is investigated, considering a third flow (the cooling air), besides the process air flow and the regeneration one. A cross-flow heat exchanger between process air and cooling air is used; the cooling air, cooled by an adiabatic humidifier, is aimed to precool the process air exiting the DW. The relevant influence of the heat exchanger and of the humidifier, as well as that of the chiller performance, on global primary energy requirements, water consumption and CO2 equivalent emissions of the system is experimentally evaluated.

A Preliminary Assessment of the Competitiveness of Wave Energy Technologies: A Regionally Detailed Analysis

In this paper, a regionally disaggregated global energy system model treating the electricity supply sector in detail is used to examine the competitiveness of wave energy technologies for each of 48 world regions over the period to 2050 under a constraint of halving global energy-related CO2 emissions in 2050 compared to the 2000 level. It is first found that wave energy continues to be uncompetitive until 2050 due to (1) its high cost and (2) the large seasonal variability of wave power. Even if the reference wave electricity generation costs are assumed to be reduced by 90%, the latter factor severely limits the market penetration of wave energy technologies. It is then found that the UK and Ireland, Australia and New Zealand, Japan, South Africa, the western US, Latin America, Canada, and Spain and Portugal are the regions promising for wave energy deployment. Not only low-cost and abundant wave energy resources, but also the peak electric load in winter, the relatively small seasonal variability of wave power, and/or the low competitiveness of power sources substitutable by wave energy are the reasons for this.

Optimized Integration of Renewable Energy Technologies Into Jordan's Power Plant Portfolio

Jordan has experienced a significant increase of peak load and annual electricity demand within the last years due to economic development and population growth. The experienced growth rates are expected to continue during the next decades, making large investments in new power plant capacity necessary. Additionally, when gas supply from Egypt was interrupted several times and crude oil world market prices increased simultaneously, recent years have shown painfully that a power supply exclusively based on fossil fuel imports is subject to a very high risk and can have a strong negative impact on the national budget. Electricity-sector authorities are therefore looking for suitable solutions to keep up with the increasing electricity demand, to make Jordan more independent from fossil fuel imports, and to provide electricity at reasonable prices in the future. This paper presents a methodology for the optimized integration of renewable energy (RE) technologies into Jordan's existing power plant portfolio. The core of the methodology is the mixed integer linear optimization program REMix-CEM, developed at the German Aerospace Center (DLR), which optimizes capacity expansion and unit commitment of RE and conventional power generation technologies simultaneously. After describing Jordan's electricity sector and the available RE resources, the developed methodology and the results are presented. The paper shows that by the year 2022, Jordan could generate at least 47% of its electricity demand by a well-balanced mix of concentrating solar power, utility-scale photovoltaics, and onshore wind power. This scenario would maintain the security of electricity supply, absorb present growth rates of power generation costs, and make Jordan significantly more independent of fossil fuel imports.

An Empirical Mode Decomposition Approach to Peak Load Demand Forecasting

An accurate and reliable electric load forecasting model is very essential for efficient and effective operation of the Electricity Supply Industry (ESI). Several single models have been developed for electric load forecast for ESI but it is becoming increasingly difficult to obtain accurate forecast by these models because of the volatility coupled with the nonlinear and non- stationary nature of electric load series. In this paper, we propose a novel Electric Peak load forecasting model that combines empirical mode decomposition (EMD) and artificial neural network (ANN). The propose model involves three stages of development. In the first stage, the historical load data obtained from Power holding company of Nigeria (PHCN), Bida is decomposed into several intrinsic mode functions and a residue component using the EMD sifting process. The second stage involves building separate neural network models for each of these IMFS and residue component and the last stage involves combining the predictions from these models and making forecast. When the forecast from this model is compared with that obtained from a conventional neural network model, it was observed that the proposed model outperforms the conventional neural network model, by 2.3% for the whole year model and by 1.8% for the weekday model, judging by the forecast accuracy of both models.

Study on Energy Materials with the Impact of Using Chilled Water Storage Systems on the Performance of Air Cooled Chillers

In the air conditioning (AC) industry chilled water storage (CWS) systems are one form of cool thermal storage technology that can be used to time shift the electrical load of the system from the peak day periods to off peak night time periods. In this paper the data for the actual exported and generated electrical energy obtained for the power stations has been used to estimate the electrical energy consumption and the peak electrical load of AC systems. It is estimated AC systems represent about 62% of the peak electrical load. The results demonstrate that CWS can reduce the peak electrical load of a chiller in an air cooled AC system by up to 100% and reduce the nominal chiller size by up to 33% depending up on the operating strategy adopted. This is achieved with only a 4% increase in power consumption of the chiller for all CWS strategies except for full storage where the energy consumption actually decreases by approximately 4%.

The zero-peak house: Full-scale experiments and demonstration

Houses represent a substantial fraction of the summer peak electrical load, and therefore measures to reduce peak demand at the household level may be valuable in stabilizing the grid and lowering peak costs. We conducted studies in geometrically-identical, side-by-side full-scale detached houses. One house was operated in a conventional manner and provided a typical reference case. In the other house we applied a variety of measures designed to reduce or shift load from peak periods. Our results demonstrated that a combination of practical operational modifications (air-conditioner cycling, doing laundry later in the evening) and commercially-available technology (exterior shading, modest PV array, energy-efficient lighting) was able to dramatically reduce (in some cases to zero) the peak electrical demand from the grid on the hottest days of the year.

Capacity Payment Impact on Gas-Fired Generation Investments under Rising Renewable Feed-In -- A Real Options Analysis

We assess the effect of capacity payments on investments in gas-fired power plants in the presence of different degrees of renewable energy technology (RET) penetration. Low variable cost renewables increasingly make investments in gas-fired generation unprofitable. At the same time, growing feed-in from intermittent RETs amplifies fluctuations in power generation, thus entailing the need for flexible buffer capacity - currently mostly gas-fired power plants. We use a real options approach to evaluate investment decisions and timing of a single investor in gas fired power generation. We investigate the necessity and effectiveness of capacity payments. Our model incorporates multiple uncertainties and assesses the effect of capacity payments under different degrees of RET penetration. In a numerical study, we implement stochastic processes for peak-load electricity prices and natural gas prices. We find that capacity payments are an effective measure to promote new gas-fired generation projects. Especially in times of high renewable feed-in, capacity payments are required to incentivize peak-load investments.

Numerical simulation of phase change material composite wallboard in a multi-layered building envelope

Phase change materials (PCMs) have the capability to store/release massive latent heat when undergoing phase change. When impregnated or encapsulated into wallboard or concrete systems, PCMs can greatly enhance their thermal energy storage capacity and effective thermal mass. When used in the building envelope PCM wallboard has the potential to improve building operation by reducing the energy requirement for maintaining thermal comfort, downsizing the AC/heating equipment, and shifting the peak load from the electrical grid. In this work we numerically studied the potential of PCM on energy saving for residential homes. For that purpose we solved the one-dimensional, transient heat equation through the multi-layered building envelope using the Crank–Nicolson discretization scheme. A source term is incorporated to account for the thermal-physical properties of the composite PCM wallboard. Using this code we examined a PCM composite wallboard incorporated into the walls and roof of a typical residential building across various climate zones. The PCM performance was studied under all seasonal conditions using the latest typical meteorological year (TMY3) data for exterior boundary conditions. Our simulations show that PCM performance highly depends on the weather conditions, emphasizing the necessity to choose different PCMs at different climate zones. Comparisons were also made between different PCM wallboard locations. Our work shows that there exists an optimal location for PCM placement within building envelope dependent upon the resistance values between the PCM layer and the exterior boundary conditions. We further identified the energy savings potential by comparing the performance of the PCM wallboard against the performance of a building envelope without PCM. Our study shows that PCM composite wallboard can reduce the energy consumption in summer and winter and can shift the peak electricity load in the summer.

Optimisation and techno-economic feasibility analysis of hybrid (photovoltaic/wind/fuel cell) energy systems in Kerman, Iran; considering the effects of electrical load and energy storage technology

A hybrid (photovoltaic, PV/wind/fuel cell, FC) system comprising different combinations of PV arrays, wind turbine, hydrogen tank, electrolyser, and FC has been investigated for stand-alone applications. Load demand was the electrical requirements of atypical residential apartment having a total area of 500 m2 with a peak electrical load of 35 kW and a yearly load of 24.4 MWh in Kerman, Iran. The assessment criterion for the analysis was levellised cost of energy of each system configuration. National Renewable Energy Laboratory's Hybrid Optimization Model for Electric Renewable software was utilised as the assessment tool of the present study. The effect of electrical load profile on the optimisation results has also been investigated considering a demand load profile with a low peak of 12 kW. Also, a comparison was made between the hybrid (PV/wind/diesel/bat) systems and the hybrid (PV/wind/FC) system of the current study at different fuel price scenarios.

Applying Innovative Models for Forecasting Small-Area Peak Electrical Loads

The number of Distributed generators is currently increasing, and the electrical industry is trending toward regional supply-and-demand and resource integration. Thus, a model that can forecast small-area peak electrical loads is an indispensable part of power infrastructures. This study constructs innovative model for forecasting small-area peak electrical loads. The main aspects considered were the accuracy of the forecasting model and the convenience of follow-up maintenance and management of the model and data. This study used yearly peak load value and total power data from substations to construct regression tree models. These acted as models for the small-region peak electrical load of substation districts in the Taipower distribution systems. The errors of these forecasting models were substantially smaller than those of the least squares model originally used by Taipower to forecast peak load. The addition of exogenous factors was unnecessary. Additionally, our results were superior regardless of whether once or incremental models were adopted for the data. This confirms the usability of our models.