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"PEAK LOAD"
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Predicting peak loads and fuel cell generation using energy storage systems
This study utilizes real-world hospital data to analyse hospital electricity consumption patterns. By investigating the hospital’s power grid characteristics, we develop mathematical models to regulate peak and off-peak loads. Simulation results demonstrate the effectiveness of integrating fuel cells (FCs) and secondary lithium batteries under different normalized benchmark values for peak suppression, showing varied effects across different load curves. Fuel cells generate electricity through hydrogen energy reactions. Unlike traditional fossil fuels, which are highly polluting and inefficient, fuel cells convert chemical energy into electrical energy through electrochemical reactions between hydrogen and oxygen at the electrodes. Their key advantages include simple structural materials, modularity, a wide application range, ease of operation, and continuous 24-hour power generation. Through an inverter, the DC output is converted to AC and either integrated into the national grid or used to support off-peak loads. Based on the hospital’s maximum daily load demand of 2000 KW, a 2500 KW fuel cell module can fully meet daily electricity requirements while effectively suppressing peak demand for up to 12 hours. This study demonstrates the high feasibility and economic benefits of integrating fuel cells (FCs) and secondary lithium batteries for hospital energy management, providing a sustainable and cost-effective solution for future power infrastructure.
Journal Article
Optimized unit commitment for peak load management with solar PV and storage under load uncertainty
The installation of solar photovoltaics (PV) has gained momentum due to growing concerns about global warming and the UN’s SDGs addressing environmental challenges. The primary objective of this paper is to evaluate and address the impacts of load uncertainty on Unit Commitment through the implementation of storage-based PV generation, wherein PV generation and energy storage operate in the proposed coordinated manner. To deal with uncertainty, a hybrid optimization technique is utilized, which combines stochastic and robust computations. Stochastic load uncertainty scenarios are generated via probabilistic Gaussian Probability Density function (PDF) approach that reside within the defined uncertainty set, as established by the robust optimization framework. The mean scenario of load uncertainty is applied to evaluate the day-ahead UC costs. The IEEE 39-bus, ten-generator system serves as the basis for this analysis. UC is optimized via Dynamic Programming (DP) in the presence of load uncertainty levels of up to 10% across three distinct case studies. Case 1 functions as the baseline for comparison as it does not include PV-storage or load uncertainty modeling. In Case 2, the influence of load uncertainty on day-ahead UC is examined for a network that excludes PV-storage. In Case 3, the system integrates the proposed coordination based PV-storage and solves UC while managing peak demand amid increasing levels of load uncertainty—specifically at 5%, 8%, and 10%. Additionally, contingency margins are evaluated across all three cases to validate day ahead 24 hours system performance and reliability enhancements. By juxtaposing the results of UC across these three cases, this study aims to analyze the implications of gradually increasing load uncertainty, load management, and peak load regulation utilizing PV-storage systems.
Journal Article
A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach
Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast the long-term performance of the electricity sector (electricity consumption, generation, peak load, and installed capacity). In this study, the model was used to forecast the aforementioned factors in Saudi Arabia for 30 years from 2021 to 2050. The historical data that were inputted into the model were collected from Saudi Arabia at quarterly intervals across a 40-year period (1980−2020). The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, which helps reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. The experimental findings indicated that the SARIMAX model has promising performance in terms of categorization and consideration, as it has significantly improved forecasting accuracy compared with the simpler autoregressive integrated moving average-based techniques. Furthermore, the model is capable of coping with different-sized sequential datasets. Finally, the model aims to help address the issue of a lack of future planning and analyses of power performance and intermittency, and it provides a reliable forecasting technique, which is a prerequisite for modern energy systems.
Journal Article
A Comparative Design of a Campus Microgrid Considering a Multi-Scenario and Multi-Objective Approach
This article proposes a plan to replace real-time power with constant power from the grid to reduce costs and reduce the impact of the micro-grid on the main grid at the same time. Most of the peak electricity consumption periods of universities or some enterprise institutions are during the daytime. If solar energy can be used reasonably at this time, it can provide a good guarantee of peak power. In this study, a grid-linked solar-plus-storage micro-grid was used to supply power to a university located in Okinawa, Japan. The non-dominated sorting genetic algorithm II (NSGA-II) was used to optimize the model size, and the loss of power supply probability (LPSP), life cycle cost (LCC), and waste of energy (WE) were taken as the optimization indicators. For this study, three scenarios were considered where the first scheme (Case 1) was a comparison scheme, which used a PV battery and real-time power from the infinity bus. Both the second and third cases used constant power. While Case 2 used constant power throughout the year, Case 3 used daily constant power. The optimal solutions for the power supply units were grouped into three cases where Case 1 was found to be the most expensive one. It was found that the costs of Cases 2 and 3 were 62.8% and 63.3% less than Case 1. As a result, the waste of energy was found to be more significant than Case 1: 70 times and 60 times, respectively. On the contrary, Case 1 had 15.2% and 16.7% less carbon emissions than Case 2 and Case 3, respectively. This article put forward the idea of constant power supply growth at the financial markets, which breaks the traditional way in which the power supply side follows the user’s consumption. While reducing costs, it reduces the impact on large-scale power grids and can also ensure the reliability of campus microgrids.
Journal Article
Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid
Increasing energy demands due to factors such as population, globalization, and industrialization has led to increased challenges for existing energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable, cheap, and easily available sources of energy. Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions. But the integration of distributed energy sources and increase in electric demand enhance instability in the grid. Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid. Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control, reinforcement of the grid demand, and generation balancing with cost reduction. But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data. Machine learning and artificial intelligence have recognized more accurate and reliable load forecasting methods based on historical load data. The purpose of this study is to model the electrical load of Jajpur, Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression (GPR). The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past, current, and future dependent variables, factors, and the relationship among data. Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead. The study is very helpful in grid stability and peak load control management.
Journal Article
Maximizing the Electricity Cost-Savings for Local Distribution System Using a New Peak-Shaving Approach Based on Mixed Integer Linear Programming
The objective of this study is to perform peak load shaving at a virtual power plant (VPP) to maximize the electricity cost-saving for local distribution companies (LDCs) while satisfying the necessary operational constraints. It can be achieved by implementing an efficient algorithm to control the conservation voltage reduction technique (CVR) with embedded energy resources (EERs) to optimize electricity costs during peak hours. EERs consist of distributed energy resources (DERs) such as solar and diesel generators and energy storage systems (ESSs) such as utility-scale and residential batteries. An objective function of mixed integer linear programming is formulated as the electricity cost function. Different operational constraints of EERs are formulated to solve the peak shaving optimization problem. The proposed algorithm is tested using data from a real Australian power distribution network. This paper discusses four cases to demonstrate the performance and economic benefits of the control algorithm. Each of these cases illustrates how EERs contribute differently each year, month, and day. Results showed that the proposed algorithm offers significant cost savings and can shave up to three daily peaks.
Journal Article
Optimal hybrid power plants for electric vehicle charging demand
Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.
Journal Article
Robust Operation of Energy Storage System with Uncertain Load Profiles
In this paper, we propose novel techniques to reduce total cost and peak load of factories from a customer point of view. We control energy storage system (ESS) to minimize the total electricity bill under the Korea commercial and industrial (KCI) tariff, which both considers peak load and time of use (ToU). Under the KCI tariff, the average peak load, which is the maximum among all average power consumptions measured every 15 min for the past 12 months, determines the monthly base cost, and thus peak load control is extremely critical. We aim to leverage ESS for both peak load reduction based on load prediction as well as energy arbitrage exploiting ToU. However, load prediction inevitably has uncertainty, which makes ESS operation challenging with KCI tariff. To tackle it, we apply robust optimization to minimize risk in a real environment. Our approach significantly reduces the peak load by 49.9% and the total cost by 10.8% compared to the case that does not consider load uncertainty. In doing this we also consider battery degradation cost and validate the practical use of the proposed techniques.
Journal Article