Peak Demand Growth Research Articles

Changing economics of China’s power system suggest that batteries and renewables may be a lower cost way to meet peak demand growth than coal

Concerns around reliability in China's electricity sector have rekindled interest in a traditional solution: building more coal-fired generation. However, over the past decade China's electricity sector has seen significant changes in supply costs, demand patterns, and regulation and markets, with falling costs for renewable and storage generation, "peakier" demand, and the creation of wholesale markets. These changes suggest that traditional approaches to evaluating the economics of different supply options may be outdated. This paper illustrates how a net capacity cost metric- fixed costs minus net market revenues- might be a useful metric for evaluating supply options to meet peak demand growth in China. Using a simplified example with recent resource cost data, the paper illustrates how, with a net capacity cost metric, electricity storage and solar PV may be a more cost-effective option for meeting peak demand growth than coal-fired generation.

Changing Energy Market Landscape Drives NAESB’s Industry‐Initiated Standards Development in 2024

Ten billion dollars—the amount of money the U.S. Department of Energy (DOE) estimates could be saved through the deployment of virtual power plants (VPPs), simultaneously helping to meet the estimated 200 gigawatt (GW) of peak demand growth expected by 2030.1 While there are variations within the industry on the construct of VPPs, one common definition of a VPP is “aggregations of [distributed energy resources] DERs that can balance electric loads and provide utility‐scale and utility‐grade services like a traditional power plant.”2 DOE is actively supporting VPPs, as evidenced by the issuance of the September 2023 report Pathways to Commercial Liftoff: Virtual Power Plants, as well as the announcement that same month of a $300 billion commitment to Project Hestia, an effort to make DERs, and the accompanying consumer‐facing software to enable a nationwide VPP infrastructure, more widely available to American homeowners.3 This backing, combined with recent legislative and regulatory actions, such as the Inflation Reduction Act (IRA) of 2022 and the Federal Energy Regulatory Commission (FERC) Order No. 2222, which established a framework for DER aggregations that meet minimum size requirements of 100 kilowatts (kW) to participate in organized wholesale markets, is likely to promote greater deployment and participation of DERs in the market and could result in a large influx of such resources in a relatively short time span.

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

Perspectives on peak demand: How is ERCOT peak electric load evolving in the context of changing weather and heating electrification?

This analysis characterizes the past and future evolution of peak electricity demand in the Electric Reliability Council of Texas (ERCOT) service area and assesses how these trends may be driven by changing weather and heating electrification. To accomplish this, historical ERCOT demand data from 1997 to 2021 were utilized along with weather data for the days in which peak demand occurred and from a future climate scenario. The results inform resource planners on the relationship between peak heating and cooling load and ambient temperature and present peak demand growth scenarios for 2025 through 2050. We found that historically, summer peak demand growth has been generally stable and approximately linear with time. Conversely, the winter peak demand growth has been less consistent, varying much more around the broader trend. These phenomena are likely consequences of temperatures that were fairly constant on summer peak demand days, but varied widely on winter peak demand days. The erratic nature of winter peak demand is also likely caused by the fact that electrical heating equipment becomes increasingly inefficient at lower temperatures, as is demonstrated by the polynomial relationship identified between per capita winter peak demand and heating degree days. Additionally, historical winter peak demand was shown to be growing more quickly than summer peak demand. This phenomenon is likely the result of increases in electrical efficiency of cooling and increases in electricity consumption that result from the rising penetration of electrical heating equipment that replace gas furnaces. Future peak demand scenarios indicate that winter peak demand will remain more erratic and will sporadically surpass summer peak demand between 2025 and 2050. Thus, resource planners in ERCOT should place less certainty on winter peak demand projections and an increased level of winter preparedness on both the supply and demand sectors appears warranted for resource planners. These findings might foreshadow future resiliency challenges that other regions will face as electric heating equipment is deployed in place of boilers or furnaces for decarbonization purposes.

A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data.

A Case Study of Grid-Connected Solar Farm Control Using Artificial Intelligence Genetic Algorithm to Accommodate Peak Demand

The continuous rise in peak electricity demands is now considered to be one of the most prominent power generation problems. The need to increase generation capacity caused by peak demand growth is a critical economic issue due to the financial burdens associated with increasing capacity. Jordan is a good example of the problems caused by growing peak demand. As a small, aid-dependent country already suffering from financial and environmental issues, the number of Syrians seeking refuge in Jordan has created a strain on the country’s energy resources. This study examines the effect of installing a Photovoltaic (PV) solar farm at the Amman East Power Plant in order to offer a solution to the continuous growth in peak demand. As part of the research reported here, three mathematical representative models have been created and tested using real solar radiation, energy generation, and peak demand data. These results then were used to build a model based on an artificial intelligence Genetic Algorithm (GA) that could be used in predicting, analysing and calculating the best solar farm size in order to keep the generation curve as flat as possible with the least cost. Subsequently, an iterative simulation of the processes has been done to test the model different chromosomes: the optimum configuration of the model ensures that the annualized cost of the system (ACS) is minimized, while the peak demand is lessened as much as possible.

An Evaluation of Electricity Net Load Profiles and the Baseload Generation Concept

Least cost planning and flattening system demand through demand-side management (DSM) was once a driving concept for system planning. This planning approach addressed the idea that flat demand profiles were preferred because they could be served efficiently by baseload generators such as pulverized coal and nuclear plants. New generation construction was driven by peak demand growth, and thus flattening loads delayed new construction and increased baseload capacity factors. Today, primarily due to the focus on renewable portfolio standards (RPS) and subsidies for renewable power, the concept of flattening a system demand profile appears to have fallen in priority. This has resulted in concerns such as the infamous “duck curve” which creates a challenge for California utilities as the unequal match between demand and intermittent renewable resources have created net loads for dispatchable resources to serve at higher costs. This paper provides an analysis of net load profiles (NLP) for several power system regional transmission organizations (RTOs): California (CAISO), Midcontinent Independent System Operator (MISO), PJM Regional Transmission Organization, and the Electric Reliability Council of Texas (ERCOT). The objective of this study is to answer the question: Would targeting a level demand profile and high baseload generation result in more efficient power systems, better balancing the use of renewable energy resources (RERs) and effective baseload capacity (EBC)? In answering this question, an analysis was done by simulating the RTO power systems in 2030 as they are estimated to operate after RPS goals have been met by the states in each respective RTO. This future scenario is compared to another scenario in which it is assumed that there are no RPS regulations, and thus no RPS resources supplying electricity in 2030.

Long term load forecasting accuracy in electric utility integrated resource planning

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electric utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. We review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.

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.

Assessing the technical impact of integrating largescale photovoltaics to the electrical power network of Bahrain

Electricity demand of Bahrain is met almost entirely by gas turbine power stations at present. The peak demand growth rate of around 6.5% per year necessitates installing new generation in near future. Bahrain’s rich natural solar resource could be exploited to meet this need. However, so far, no detailed studies on the grid integration of large scale photovoltaics (PV) systems have been carried out. Potential impacts of integrating a 1MW PV plant to the power network of Bahrain is examined in this paper by means of a systematic modelling analysis using PVsyst and PSSE. Results demonstrate how PV systems can exploit the advantage provided by the weather of Bahrain to match the peak demand. Other concerns addressed in this work are impact of PV generation during times when electrical power network is more susceptible to voltage limit violations and impact of fault current contribution from the PV plant to the security of the network considered. The overarching view from the results of these grid integration studies is that the outlook for network integrated PV in the future In Bahrain is positive. It is hoped that the results of this work would inform Bahrain’s utility’s policies on PV systems.

Statistical analysis of drivers of residential peak electricity demand

Growth in peak electricity demand poses considerable challenges for utilities seeking to ensure secure, reliable yet affordable energy provision. A better understanding of the key drivers of residential peak electricity demand could assist in better managing peak demand growth through options including demand-side participation and energy efficiency programs. However, such analysis has often been constrained by the limited data available from standard household metering, as well as typically low direct engagement by utilities with households regarding their energy use. This paper presents a study analysing and modeling residential peak demand in the greater Sydney region using data from Australia’s largest Smart Grid study to date. The dataset includes household level half hour consumption matched to surveyed information including housing type, demographics and appliance ownership. A range of statistical and modeling techniques are applied to determine key drivers for household demand at times of network peaks. The analysis and model quantify how different factors drive residential peak demand on hot summer days. Key drivers identified include air-conditioning ownership, the number of household occupants, swimming pool ownership, and clothes dryer usage. Finally, the model is used to investigate the potential aggregate network peak implications of changes in household demographics and appliance ownership.

Electricity peak demand in Uganda: insights and foresight

Availability of reliable energy supply plays a critical role in the social, economic, and cultural transformation of society. The Uganda electricity sector has suffered long standing supply side constraints that resulted in suppressed demand and outages. Recent developments, including the completion of the 250 MW Bujagali project in 2012, have resulted in sustained growth in peak demand. However, this growth in peak demand appeared to stagnate by 2013. This study examines the recent trends as well as forecast the medium term path of electricity peak demand in Uganda. This study uses descriptive data exploration analysis and polynomial functions augmented by empirical estimations of structural break equations to account for the observed trends in electricity peak demand. The study applies the double exponential forecasting model to forecast total peak electricity demand. The results show that the recent surge in electricity peak demand is due to increased electricity exports. Moreover, the results show a shift in electricity demand from peak to nonpeak time-of-use, possibly due to changing consumption patterns in the industrial sectors. The study draws two major conclusions. First, the growth in Uganda’s electricity demand in general and peak demand in particular has not stagnated as such but rather partially shifted from peak to nonpeak time-of-use zone. Second, electricity exports have contributed to growth of electricity peak demand. Importantly, higher electricity exports need to be considered in line with the system capacity given the current electricity spinning reserves of Uganda are less than 15 % of Uganda current installed capacity.

Australian retail electricity prices: Can we avoid repeating the rising trend of the past?

After a stable or declining real trend that persisted for more than half a century, Australian retail electricity prices have experienced a substantial increase, in real terms, since 2007. This has mainly been driven by increases in the cost of electricity distribution and to a lesser degree in the cost of electricity generation. Reducing greenhouse gas emissions, which is a bipartisan political goal in Australia, will likely deliver further increases in generation costs due to the expected higher cost of low emission technology. Participating in global negotiations on emission reduction targets and designing efficient policy mechanisms have been a major focus of governments over the last several decades. In contrast, managing distribution system costs has received less attention. While there were a number of factors which drove historical increases in distribution costs, management of peak demand growth could help contain or reduce the extent to which consumers, particularly households, experience further increases in distribution costs. The paper demonstrates how different combinations of carbon price and peak demand scenarios could impact future residential and industrial retail electricity prices to 2050 and discusses some behavioural and technological solutions to manage peak demand and potential barriers to their deployment.

Electric Demand Analysis of the Tunisian Network: Trends and Short Term Photovoltaic Implementation with Alternatives Prospects and Technologies

The main objective of this study is to analyze the system of electric demand in Tunisia and to propose immediate alternative solution of photovoltaic implementation that can be suitable based on available natural solar energy resources in the country to respond to the short Forecast peak demand growth and energy consumptions taking into account financial strain. On the first step, analysis of the situation based on the recent historical data is proposed. In a second step, a solution is considered, based on photovoltaic implementation and using the results of historical data analysis. The resulting benefits are highlighted: financial, technical, environmental and social. Future studies may be made to use a mix of technology and policy as well: among it combining photovoltaic and battery storage, in parallel with energy efficiency programs.

Meeting Victoria’s energy requirements in the 21st Century

Victoria has a remarkable richness and diversity of energy resources. Reserve estimates include some 430 billion tonnes of brown coal, in near-surface seams up to 230 metres thick; over 8 trillion cubic feet of as yet undiscovered gas; and perhaps 600 million barrels of undiscovered crude oil. To this may be added some of the best wind resources in the world, significant solar resources, the potential for geothermal energy resources, and significant bioenergy potential associated with Victoria’s substantial agricultural and forestry industries. Victoria is also deeply enmeshed in the national grid, so provided that reasonable investment in network maintenance and security continues, and demand growth (including peak demand growth) is restrained by energy efficiency policies, network security should remain high. Setting aside temporary disruptions due to infrastructure failures, running out of energy is not a problem that Victoria will face during the 21st century, or perhaps ever.

The challenge of adapting centralised electricity systems: peak demand and maladaptation in South East Queensland, Australia

South East Queensland’s (SEQ’s) centralised electricity system is under great pressure to adapt. Climate change is converging with socio-economic, demographic and technological changes to create a ‘perfect storm’ for the region’s electricity system. Distribution networks are particularly affected, with these factors contributing to tremendous peak demand growth, about double the rate of growth in average demand in recent years. This paper reviews how Australia’s electricity system is adapting to multiple drivers of peak electricity demand. We use socio-technical transitions theory to understand the temporal interconnected social and technical dimensions of adaptation in this setting. Specifically, we present an historical narrative of the emergence of centralisation in Australia and outline the peak demand problem in SEQ and review adaptation options from the international literature. We also analyse the interactions between key social groups and their adaptation responses over the past decade. Our analysis shows that adaptation has become a contested process between supply-chain actors and end-users, each with different economic objectives, adaptation needs and capacities. The resulting adaptation dynamic that is emerging shows worrying signs of maladaptation. Implications for market governance and urban policy and research are discussed.

The Boomerang Paradox, Part I: How a Nation's Wealth Is Creating Fuel Poverty

A characteristic of advanced economies like Australia is continual growth in household income and plunging costs of electric appliances, resulting in rapid growth in peak demand. The power grid in turn requires substantial incremental generating and network capacity, which is utilized momentarily at best. The result is the Boomerang Paradox, in which the nation's rising wealth has created the pre-conditions for fuel poverty.

Electricity restructuring [the business scene

The Islamic Republic of Iran currently manages an electricity sector with about 40,000 MW of installed capacity, which serves more than 13 million end users through a 36,000-km high-voltage transmission interconnected grid and a more than 50,000-km medium-voltage distribution network. This fairly large power system with an annual peak demand growth rate of about 10%, one of the highest rates in the world, makes the movement toward restructuring the electricity industry of Iran unavoidable, mainly for two reasons: 1) it is expected that the restructuring and consequently the privatization would improve the performance and efficiency of the present industry 2) it is expected that the development of a new competitive paradigm in the electricity industry could make the sector more attractive for potential independent investors. Based on these ideas, the restructuring studies primarily were begun in early 1990. The studies had two main directions: providing legal foundations and determining the main implementation steps. This article discusses these two directions