Annual Peak Load Research Articles

Impact of varying usage profiles for different building envelope types with all-electric HVAC systems

In buildings, the usage profile has a significant impact on the operation of different building HVAC systems depending on building envelope designs. This study presents results of a larger study that looked at the influence of building envelope design and HVAC system configuration on the energy performance of a building with varying usage profiles. In a previous paper, the significant impact of thermal zoning methods was identified on the annual energy use in all-electric HVAC systems due to varying usage rates and climate. In this paper, the impact of usage variations considering building thermal mass (i.e., lightweight structure vs. heavyweight structure), window-to-wall ratio (10–40%), and HVAC system type (i.e., packaged single zone and variable air volume) in two different climates (i.e., hot-humid vs. cold-humid) are presented. The results of this study show that the energy use variations from varying usage profiles differed significantly depending on building thermal mass, window-to-wall ratio, and HVAC system type in two different climates (i.e., Houston, TX, and Chicago, IL). The results of this study will contribute to a better understanding of the hidden impacts of building envelope design and HVAC system type on annual energy use and peak load conditions.

Performance evaluation of underground thermal storage integrated dual-source heat pump systems

The increasing demand for electricity stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions. Integrating thermal energy storage (TES) in building heating/cooling systems, which consume considerable electricity, can mitigate the challenges to electric grids. This study reports on a novel thermal energy storage device integrated heat pump system to reshape the building electricity demand profile while maintaining thermal comfort. The annual performance of the proposed system has been evaluated through a dynamic system simulation with high fidelity in the Modelica platform. The dynamic model of the novel hybrid component named ‘dual purpose underground thermal battery’ was developed and validated. It was then incorporated into the system model. Given a time-of-use tariff, a rule-based control strategy was designed to shift the electric demand and switch the heat pump source for a typical single-family house in different climate zones of the United States. The system performance of the new TES-integrated dual-source heat pump was compared with that of a conventional air-source heat pump system. The results indicate that the proposed system can reduce the annual HVAC electricity cost by up to 52% while saving 45.2% on electricity consumption. In the Northern areas, the annual peak load of the HVAC system can be reduced by 64.9%. However, this reduction is less in the Southern areas as the system’s higher efficiency in winter dominates the overall energy-saving potential.

Modeling of Long‐Term Load Forecast in Jordan Based on Statistical Techniques

The paper proposes a mathematical model for long‐term load forecast (LTLF) based on parametric and time series statistical techniques. The flowchart of the proposed algorithm was also presented. The multiple linear regression (MLR) as well as the autoregressive integrated moving average (ARIMA) model with different model orders are employed in load forecasting. Historical data from 1990 to 2022 were utilized in the model implementation and validation. The input data imply gross domestic product (GDP), oil prices, population, and the energy from renewable energy projects as independent variables, and the annual peak load is the dependent variable. The results obtained by MLR show that population and GDP have a positive impact on electricity demand, whereas the oil price and the penetration of the renewable energy have a negative impact on electricity demand. In ARIMA, the load forecast is estimated based on error (residual) estimation that is determined based on the time lag operator and autoregressive and moving average coefficients. The ARIMA (p, d, and q) model with six different model orders is investigated. The Bayesian information criteria (BIC) and Akaike information criteria (AIC) indices are used as a measure in the selection of the appropriate model for prediction. The comparison between the six model’s scenarios shows that ARIMA (1, 1, 1) is the best model that fits the time series with minimum error and with the lowest BIC and AIC. The RMSE, MAPE, MSE, and MAE provided by ARIMA (1, 1, 1) are 3.57%, 2.55%, 0.13%, and 54.36, respectively, whereas the AIC and BIC are 386.8 and 394.0, respectively. A comparison between ARIMA and MLR shows that ARIMA is better than MLR in terms of time series fitting and error level. The LTLF forecast period covering the period 2024–2035 considers three scenarios of future development in the country: the normal (medium forecast), the optimistic (high forecast), and the pessimistic (low forecast). The forecast is made in the three directions (i.e., medium, high, and low) to avoid the ambiguity and uncertainty that may occur in the data input used in forecasting the electricity demand. A comparison between the MLR and ARIMA in the last year 2035 shows a small deviation between the two methods with the forecasted values in the range between 5480 MW and 5520 MW, respectively.

Hosting Capacity Estimate Based on Photovoltaic Distributed Generation Deployment: A Case Study in a Campus of the University of São Paulo

Distributed generation, which is mainly deployed with PV systems that benefit economically prosumers, has soared in use in Brazil. Despite this, PV capacity in excess may cause technical issues which concern planning engineers who have adopted rules of thumb to screen interconnection requests without any detailed study. Recently, the hosting capacity concept has been employed to assess how much PV capacity a distribution grid can host without deteriorating grid parameters, reliability, or power quality. A steady-state and worst-case-based scenario was used to run deterministic power flow simulations to estimate the hosting capacity of a specific radial circuit at a campus of the University of São Paulo, referred to as “USP-105”. Although the result may be not completely accurate, it was found that USP-105 can accommodate 103% of its peak load or 4970.6 kW of PV power, which reduced the circuit’s annual peak load by 9%. Another finding was that hosting capacity increased when PV-DG deployment was dispersed along the circuit rather than concentrated on a single location (e.g., closest, or furthest to the substation). Utilities may therefore benefit from a simple and quick assessment to obtain an overview of how specific circuits behave on PV deployment and indicate which locations are technically more beneficial.

Optimizing Hybrid Power for Manpura Island: A Case Study in Bangladesh

In the remote and isolated island Manpura in southern Bangladesh, located within the Bhola district and surrounded by the Meghna River, most communities rely on farming and fishing for their livelihoods. Due to its isolation, the island is not connected to the mainland's electrical grid. In this study, a freestanding green hybrid energy system was proposed for the Manpura island using HOMER software to meet the electricity needs of its 17,016 households. The average load for the system is 9,149.87 kWh per day, with an annual peak load of 2,112.6 kW. The system would utilize PV panels, hydrokinetic energy, biogas, wind turbines, storage, and a converter, with a total Net Present Cost (NPC) of 23,796,260.00 and a Cost of Energy (COE) of $0.5515. This hybrid energy systems would provide a sustainable and cost-effective long-term power solution for the island while preserving its natural beauty.

Hybrid solar thermal and heat pump systems in industry: Model based development of globally applicable design guidelines

Solar thermal heating systems and heat pumps are key technologies for decarbonizing low temperature industrial heat demand. Fluctuating solar irradiance, limited heat source capacity or limited availability of renewable electricity often limit the potential of the single technologies. To maximize the share of renewable heat supply, the combination of both technologies is a promising option. This study takes the first steps towards filling the research gap of missing guidelines for preliminary design or feasibility studies. A simulation tool based on idealized component models is used to compare technical and economic performance for 30,240 parameter combinations, that define various global reference applications, system designs, and economic frameworks. The heat sink and source temperatures, and the energy prizes have the highest impact on the economic performance. The potential for technical optimization is small. If the fraction of both heat generators together is close to 100 %, SCOP differences between different hydraulic concepts are neglectable. The most viable option for optimizing the system's LCOH is to reduce the heat pump capacity to about the half of the annual peak load. Due to recent increases in energy prices, a complete decarbonization is economically feasible without subsidies for most of the applications studied.

Electricity market modeling considering a high penetration of flexible heating systems and electric vehicles

The electrification of the heating and transportation sectors is a major building block in Germany’s effort to reduce carbon dioxide emissions. The additional loads and flexibilities resulting from the anticipated large-scale integration of the heating and transportation sectors necessitate an adequate representation in electricity market modeling. First, the paper presents a comprehensive modeling approach of the heating sector in a pan-European electricity market simulation considering both small-scale building heat pumps and large-scale conversion units with thermal energy storages in district heating networks. The operation of small-scale building heat pumps is emulated using an electro-thermal 1R1C building model integrated into the economic dispatch formulation while the operation of units in district heating networks is restricted by additional constraints resulting from the combined heat and power output, local heat load coverage and possible heat power transfer between heating networks. The transportation sector is mapped using previously published models by the authors including both controlled and fast charging processes. Subsequently, the load shift potentials resulting from the market-integration of the heating and transportation sectors and their impact on the pan-European electricity market with focus on Germany based on the three target years 2030, 2040 and 2045 are examined. Furthermore, by applying a Monte Carlo simulation to map possible future primary energy source prices a major uncertainty dimension is addressed. Considering the current German electricity market design, the integration of the two sectors lead to a moderate increase in future annual electricity demands and a substantial increase in market-based peak load. The resulting annual peak load from power-to-heat devices account to 22 GW (2030), 53 GW (2040) and 102 GW (2045) and from electric vehicles to 12 GW (2030), 36 GW (2040) and 96 GW (2045) for the target years. Further results demonstrate the larger load-shift potentials of electric vehicles compared to building heat pumps by comparing the weighted annual average market price of the flexible loads. The primary fuel price uncertainty is examined based on the 0.25, 0.50 and 0.75 quantiles for the target years which result in annual average electricity market price spreads of over 2.16 (2030), 2.97 (2040) and 3.44 (2045) between the 0.25 and 0.75 quantile scenarios.

Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.

Operating Renewable Energy Communities to Reduce Power Peaks in the Distribution Grid: An Analysis on Grid-Friendliness, Different Shares of Participants, and Economic Benefits

Improving the control of flexible assets in distribution grids, e.g., battery storages, electric vehicle charging points, and heat pumps, can balance power peaks caused by high renewable power generation or load to prevent overloading the grid infrastructure. Renewable energy communities, introduced as part of the recast of the Renewable Energy Directive, provide a regulatory framework for this. As a multi-site energy management method, they can tap flexibility potential. The present work quantifies stimulus for renewable energy communities to incentivize the grid-friendly operation of flexible assets, depending on the shares of participants in rural, suburban, and urban grid topologies. Results indicate that an operation of the community, driven by maximizing the economic benefits of its members, does not clearly reduce the annual peak load at the low-voltage substation, while the operation strategy of a grid-friendly renewable energy community achieves a peak power reduction of 23–55%. When there is not full participation, forecasts of the residual load of non-participants provided by the distribution system operator can be considered in the optimization of the renewable energy community. For all simulation cases, the economic benefit between the two operation strategies differs by less than one percent, resulting in a very low additional incentive required for grid-friendliness in terms of reduced peak power. Thus, grid-friendly renewable energy communities might be a cost-effective way to defer future grid reinforcements.

Research on the design and influence of unit generation capacity adequacy guarantee mechanism in the power market

With the advancement of power system reform and the improvement of renewable energy penetration, the situation of power generation enterprises and the generation capacity adequacy of power system are facing challenges. It is urgent to ensure reliable power supply in the market through new evaluation methods and guarantee mechanisms. Firstly, this paper proposes an evaluation method of generation capacity adequacy for multi-types units, which quantitatively calculates the adequacy capacity of each unit and the generation capacity adequacy of the system. Secondly, this paper designs a unit capacity compensation mechanism adapted to the initial stage of Power Market construction, which includes capacity pricing model, compensation capacity quota model, capacity fee settlement model. Among them, the capacity pricing model considers the investment cost of marginal units operating during annual peak load, while the compensation capacity quota model considers the actual generation capacity of units and the actual demand of the system. Finally, this paper takes a certain region of China as an example to verify the effectiveness and adaptability of the mechanism. The results show that the capacity adequacy guarantee mechanism designed in this paper can effectively reflect the overall level of power system generation capacity adequacy, fairly distinguish the reliability value of various generation units, help power generation enterprises recover fixed costs within the planned investment payback period and ensure the system generation capacity adequacy.

Load characteristics forecasting of Hubei power grid up to year 2030 with the development of electric vehicles

The load characteristics of a power grid significantly change with the increase in the number of electric vehicles (EVs). In this study, we present a method for predicting the long-term load characteristics of Hubei Power Grid (A large power grid in China with annual power consumption of 207.1 billion kWh in 2020) up to until year 2030 with the development of EVs. The predicted load characteristics include the annual peak loads and hourly load curves of typical days for different seasons in target years 2025, and 2030. The proposed long-term load forecasting method uses saturated load prediction with grey Verhulst model and Monte Carlo simulation. The future impacts of the charging load of EVs on the grid are discussed in detail.

A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively.

How will climate change affect the peak electricity load? Evidence from China

The increasing peak load caused by climate change is challenging the electricity system reliability, and an accurate forecast of peak load can provide necessary support for the infrastructure investment and resilience enhancement. To support China's long-term power system planning, this study estimates the response functions of annual peak loads to maximum temperatures in China and then predicts the future peak loads under different scenarios. Key findings are summarized as follows: (1) There is a significantly positive correlation between the provincial highest temperatures and peak loads in China, and an increase of highest temperature by 1 °C will, on average, rise the peak load by 0.385 GW (2) The impacts of maximum temperature on the peak load vary substantially among different regions, and the impacts are the most significant in the Eastern China region. (3) The adoption and penetration of air conditioners is an important channel to materialize the impacts from temperature change to peak load. (4) The national peak load is forecasted to reach between 3807 GW and 6815 GW by the end of this century, which will require an additional infrastructure investment of 275–617 billion yuan per year.

A Generalized Long-term Load Forecasting Model Using Modified Grey Wolf Optimization Algorithm

Annual accurate predicting is important for long-term planning of the electricity company because it always seeks to achieve the reliability and sustainability for the electrical services. The process of long term load forecasting (LTLF) is implemented in several manners depending on random variables affecting the consumption of the load. This paper introduces two different strategies that have been utilized for annual electrical peak load (PL) for Jordan. The first strategy used in this paper is based on different historical features gathered form national electric power company (NEPCO) such as (population growth, the gross domestic product of the country and the historical annual PL). The second strategy is based on the historical electrical peak load only. The work includes a comparison process using three different optimization algorithms particle swarm (PSO), grey wolf (GWO) and modified grey wolf (MGWO-based on the concept of aligned) to train a feed forward neural network (FFN). The proposed MGWO-FFN gives the best results of both the twostrategies than GWO-FFN and PSO-FFN. Results of the two strategies are presented and indicate the superiority of the second strategy on the first one.

A simulative framework for a multi-regional assessment of local energy markets – A case of large-scale electric vehicle deployment in Germany

The increasing share of distributed energy resources gives rise to new opportunities for deploying innovative business models and coordination schemes within sustainable energy systems. Different concepts entail different implications at socioeconomic, technical and institutional level. Hence, their thorough assessment is key to understanding their actual potential as enablers of the energy transition. Considering this background, we focus on local energy markets as an increasingly discussed approach for coordinating distributed energy systems and introduce a simulative framework for enabling a multi-regional assessment of this concept. Local energy markets bear the potential for increasing the active participation of end consumers, which could increase their acceptance for energy projects in general and their returns on investment, as well as for reducing the peak load on increasingly congested electrical grids by enhancing local energy balancing. We evaluate these hypotheses for twelve representative German regions, for which we formulate assumptions regarding the energy demand as well as the shares of distributed energy resources that are consistently aligned with an overall European energy scenario envisaging a rapid growth of electric vehicles in Germany. For this purpose, we enhance an existing framework for the assessment of local energy markets in order to be able to include the flexibility of the electric mobility sector in local trade activities. The simulation results show that local energy markets have a significant impact on energy systems: First, local trading increases the economic benefits over all participants, who would otherwise only be able to use their generation for self-consumption or direct marketing in central energy markets. Second, local energy balancing increases on average by60%over all regions. Third, infrastructural relief of the overlaying transmission grids can be accomplished by reducing the yearly peak load at the point of common coupling by39%on average and at the most by97%.Furthermore, we find that including electric vehicles in local market activities does not alter but rather reinforces these effects.

A statistical methodology to identify imbalance-induced capacity wastes for LV networks

Phase imbalance (i.e., load imbalance among the three phases) causes inefficient use of three-phase distribution assets, resulting in additional reinforcement costs (ARCs) compared to if the three phases were balanced. However, two problems remain unresolved when calculating the ARC: 1) previous ARC formulas assume that the three-phase total peak and the single-phase peak current occur simultaneously, thus causing an underestimation of the ARC; and 2) previous formulas calculate ARC as a point value, using one year's peak load. However, the point value is not credible in supporting making investment decisions on phase balancing. This is because the growth of the yearly peak load is inconsistent over the years – the growth could be positive or negative over the years for low voltage (415 V, LV) networks. To address the above problems, this paper originally develops 1) an updated ARC formula; and 2) a customised approach, named as the cluster-wise probability assessment, to deliver a distribution and confidence range of ARCs for any given LV network. This approach only requires the yearly average current and single-phase peak current, making it applicable to the majority of the LV networks that have a minimal amount of data. Case studies also identify a counterintuitive finding of how the yearly single-phase peak current and the yearly average phase current impact the ARCs.

Analysis of Age Transformer Due to Annual Load Growth in 20 kV Distribution Network

Distribution transformer is a component in distributing electricity from distribution substations to consumers. Damage to distribution transformers causes continuity of customer service to be disrupted (power cut or blackout occurs). The length of the PLN electricity network requires a transformer to distribute electricity to serve consumers and how to maintain the transformer. The daily load curve of a peak load for housing, shops and factories / industries varies. Load served 200 kVA distribution transformer cannot serve the load on housing, shops and factories / industry. The method used is the replacement of a distribution transformer with a capacity of one stage greater or the replacement of a distribution transformer with a capacity of two levels larger. The distribution transformer carried out by the research is a capacity of 200 kVA replaced by 250 kVA. The ability of a distribution transformer cannot accommodate a load which will increase as an area is advanced. Observations made by calculating the age of the transformer by assuming the annual load growth (r) = 3% = 0.3. Annual peak load (P) = 1.8 p, u increase in oil temperature at peak load (θo = 96.21 0C; 84.16 0C). The increase in the hottest temperature above the oil cover, the increase in the temperature of the hottest place above the oil (θg = 20 0C; 20 0C). The ratio of the load loss to the nominal load excitation loss (Q = 3; 30). By assuming the values of these methods it can be estimated that the life of a distribution transformer is 20 kV, a capacity of 200 kVA is 18 years.

The effect of driver variables on the estimation of bivariate probability density of peak loads in long-term horizon

It is evident that developing more accurate forecasting methods is the pillar of building robust multi-energy systems (MES). In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variables in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, gross domestic product (GDP), and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.

How to model European electricity load profiles using artificial neural networks

We present a method to create synthetic, weather-dependent, annual electricity load profiles for European countries in hourly resolution using artificial neural networks as a necessary basis for long-term forecasts. To this end, we train fully connected dense artificial neural networks with 5 hidden layers and 1,024 hidden nodes per layer using historic data for Germany from 2006 to 2015. Input parameters used in the model comprise calendrical information, annual peak loads and weather data. We benchmark our results against the current state-of-the-art method to generate synthetic load profiles used in mid-term adequacy forecasts published by the European Network of Transmission System Operators (entso-e). For validation year 2016, our approach shows a mean absolute percentage error of 2.8%, whereas the method as used by entso-e shows an average error of 4.8%. We then conduct forecasts for Germany, Sweden, Spain, and France using our synthetic load profiles for scenario year 2025 to demonstrate their pan-European applicability. Finally, we assess parameter variations that demonstrate high influences of outdoor temperatures and wind speed on the electricity load. Our approach can help to increase prediction accuracy of future electricity loads as electricity load profiles are a necessary input for these forecasts.

Long Term Load Forecasting Based on Hybrid Model of Feed Forward Net and Modified Grey Wolf Optimization

This paper presents a hybrid modified grey wolf optimization (MGWO) algorithm with the feed forward net (FFN), named MGWO-FFN, for solving electrical load forecasting. The proposed model is implemented with two stages: firstly, MGWO algorithm estimates the optimum variables of the FFN through the pre-determined training samples. Then the adapted FFN is tested with the remaining other samples and is utilized to predict the electrical peak load (PL). The proposed algorithm is investigated on two real cases (i.e. predicting the annual total electrical load consumption of Beijing's city and the annual PL consumed in Egypt). To prove the superiority of the proposed algorithm, MGWO is validated by comparing with algorithm including classical GWO and PSO algorithms. Both of Beijing's and Egypt's cases results indicate that the proposed MGWO-FFN algorithm outperforms the others where less mean square error (MSE) and more accuracy are obtained compared to the error that yields using the other two algorithms.