Customer Load Profile Research Articles

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into ‘M-shaped’ and ‘square wave-shaped’ load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the ‘M-shaped’ pattern fixes a 2-cycle operation per day due to battery life, while the ‘square wave-shaped’ pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits.

Demand Response Economic Assessment with the Integration of Renewable Energy for Developing Electricity Markets

Electricity disparity in sub-Saharan Africa is a multi-dimensional challenge that has significant implications on the current socio-economic predicament of the region. Strategic implementation of demand response (DR) programs and renewable energy (RE) integration can provide efficient solutions with several benefits such as peak load reduction, grid congestion mitigation, load profile modification, and greenhouse gas emissions reduction. In this research, an incentive and price-based DR programs model using the price elasticity concepts is proposed. Economic analysis of the customer benefit, utility revenue, load factor, and load profile modification are optimally carried out using Freetown (Sierra Leone) peak load demand. The strategic selection index is employed to prioritize relevant DR programs that are techno-economically beneficial for the independent power producers (IPPs) and participating customers. Moreover, optimally designed hybridized grid-connected RE was incorporated using the Genetic Algorithm (GA) to meet the deficit after DR implementation. GA is used to get the optimal solution in terms of the required PV area and the number of BESS to match the net load demand after implementing the DR schemes. The results show credible enhancement in the load profile in terms of peak period reduction as measured using the effective load factor. Moreover, customer benefit and utility revenues are significantly improved using the proposed approach. Furthermore, the inclusion of the hybrid RE supply proves to be an efficient approach to meet the load demand during low peak and valley periods and can also mitigate greenhouse gas emissions.

Optimization of Peer-to-Peer Power Trading in a Microgrid with Distributed PV and Battery Energy Storage Systems

Integrating distributed generation (DG) into the main grid is a challenge for the safety and stability of the grid. The application of peer-to-peer (P2P) technology in microgrids with distributed generation is expected to facilitate increased self-consumption of distributed and renewable energy, and the rise of prosumers’ monetary benefits. A P2P energy trading model in microgrids with photovoltaic (PV) distributed generation and battery energy storage systems (BESSs) is proposed in this paper. We additionally designed a P2P electricity trading mechanism based on coalition game theory. A simulation framework of this model is presented which assumed a local community with 30 households under comprehensive constraints encompassing a customer load profile, PV system, BESSs, market signals including feed-in tariffs, and retail prices. Firstly, individual customers can post orders (purchasing orders or selling orders) and exchange information in a P2P energy trading market. Secondly, the microgrid operator can validate the orders based on how to achieve the minimum overall energy consumption in microgrids and set reasonable real-time purchasing and selling prices for P2P energy transactions. Thirdly, the orders can be automatically conducted and completed at the designed optimal price. This mechanism can be a practical solution motivating individual customers to participate in P2P electricity trading, assist with electricity cost reduction, benefit from electricity supply increases, and help the grid operators to make the most economically and socially friendly decisions.

Maximizing Savings in Electricity Bills Based on Customer’s Load Profile

With the high demand in electricity consumption nowadays, it is crucial for regulator and utilities to ensure sufficient energy supply to meet electricity demand. Electricity demand is influenced by several factors such as number of customers, customer behavior, working hours, weather condition and holidays. Integrating renewable energy technology as part of electricity generation for self consumption has indirectly provide an option to customer to reduce his electricity consumption from the grid and help to save his electricity bill. One of the simplest solutions to install renewable energy sources is by installing rooftop solar photovoltaic (PV). In this paper, the economic feasibility of installing solar PV is discussed based on commercial customer load profile. This paper also presents the suitable PV sizing based on the payback analysis based on customer load profile. A commercial customer in Petaling Jaya, Selangor is used as a case study for this analysis. This study indicates that customer will be able to reduce their electricity bill consumption with the integration of solar PV system on the rooftop of their building. Customer is able to save their monthly electricity up to 28% if a total solar PV capacity of 1256kW is installed. The payback from this study indicates the payback period is approximately around 9 years.

A Block-of-Use Electricity Retail Pricing Approach Based on the Customer Load Profile

The utilization rate of generation assets significantly affects the profits of generation companies. Since the daily load profiles of customers are not considered in conventional time-of-use (TOU) pricing, a fairness issue may arise because of cross subsidy. To address this issue, a block-of-use (BOU) electricity retail pricing approach is proposed, in which the price varies from block to block instead of varying from period to period. The aggregated daily load profile is horizontally divided into blocks according to the utilization rate of the generation assets. A block filling approach is proposed to allocate the customer load into different blocks. The contribution of each customer’s load profile to the generation assets can be quantified. A BOU pricing approach is proposed, in which the rate of each block consists of the fixed rate and the variable rate. The mathematical model of the proposed approach is formulated. The case study based on the actual load data verifies the effectiveness of the proposed approach and model. Compared to the TOU scheme, cross subsidy is resolved, the average rate of customers with friendly behaviors is low and that of customers with unfriendly behaviors is high in the BOU pricing.

Utilization of Electrical Energy Storage With Power-Based Distribution Tariffs in Households

Energy storage enables modification of the customer load profile from the grid perspective without leading to a decrease in comfort level. To meet the future challenges of the energy sector, distribution system operators (DSOs) in Finland have recently discussed power-based distribution tariffs (PBDTs) for small customers. The current distribution tariffs of small customers do not respond well to current challenges, and they require reform. The peak powers of the customers can be decreased with energy storage, and via incentives included in the PBDTs, energy storage can prove to be profitable for household customers. In this paper, we study the profitability of electrical energy storage under different pricing structures. The study simulates changes in customer consumption with a modeled storage system. Simulations consider the requirement of load forecasting in the storage control system. The results of the study show that energy storage can be profitable if the consumption includes only a small number of high peak loads during the pricing period when applying new distribution tariff structures. However, the price level of the tariffs and the tariff mechanisms affect the results.

Insights into residential EV charging behavior using energy meter data

Mass adoption of the plug-in electric vehicle (EV) technology is imperative for the rapid electrification of the transportation sector to mitigate catastrophic effects from climate change. Rapid integration of a large number of EVs will inevitably cause uncertainty and variability on the operation of the existing electric power system. There is high uncertainty on not only the speed and scale of EV adoption but also the EV energy and power requirements that depends on EV charging patterns. This study uses energy meter-level data from the San Diego region to analyze the energy load profiles of residential customers under the time-of-use (TOU) rate with and without EV charging requirements. Unlike previous forecasts on the effects of EV charging loads, the energy load profile of TOU customers with EVs reveal a “twin demand peak” where there is a peak demand during the evening hours and another at midnight. Results reveal potential issues for grid operations with greater EV adoption and the importance of careful TOU rate design.

Evaluation of Grid-Connected Micro-Grid Operational Strategies

Abstract This study investigates the operational performances of a grid-connected microgrid with integrated solar photovoltaic and battery energy storage. The study is based upon the techno-economic specifications and theoretical performance of the distributed energy resource and storage systems, as well as on measured consumer load data and electrical utility retail and distribution data for representative residential and commercial loads for the city of Vasteras, Sweden. The open-source Matlab®-based simulation tool, OptiCE, is used for performing simulations and optimization. To support the attainment of one of the objectives, peak shaving of the consumer load, a battery operational strategy algorithm has been developed to balance peak shaving and PV self-consumption. Comparisons among three types of battery, lead-acid, lithium-ion and vanadium-redox flow, are also performed. A 117 kWp photovoltaic system paired with a lithium-ion battery of 41.1 kWh capacity is the optimal solution found for the considered commercial load. The calculated battery capacity represents the best trade-off for the set multi-objective optimization problem. The simulation of this system predicts the possibility to shave the customer load profile peaks up to 20% for the month of April. The corresponding self-consumption ratio is 88%. Differences in the relationship between the load profiles and the system performance have been qualitatively noted. Furthermore, the simulation results for lead-acid, lithium-ion and vanadium-redox flow battery systems reveal that lithium-ion batteries delivers the best trade-off between total annualized cost and peak shaving performance for both residential and commercial applications.

A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing

This paper establishes a short-term decision model, based on robust optimization, for an electricity retailer to determine the electricity procurement and electricity retail prices. The electricity procurement process includes purchasing electricity from generation companies and from the spot market. The selling prices of electricity for the customers are based on time-of-use (TOU) pricing which is widely employed in modern electricity market as a demand response program. The objective of the model is to maximize the expected profit of the retailer through optimizing the electricity procurement strategy and electricity pricing scheme. A price elasticity matrix (PEM) is adopted to model the demand response. Also, uncertainty in spot prices is modeled using a robust optimization approach, in which price bounds are considered instead of predicted values. Using a robust optimization approach, the retailer can adjust the level of robustness of its decisions through a robust control parameter. A case study is presented to illustrate the performance of the model. The simulation results demonstrate that the developed model is effective in increasing the expected profit of the retailer and flattening the load profiles of customers.

Estimation of Aggregate Reserve With Point-of-Load Voltage Control

Voltage dependent loads can collectively provide a certain amount of power reserve (by virtue of the ability to change their power consumption within the stipulated voltage tolerance) which could be exploited for grid frequency regulation through voltage control at the substation/feeder or at the point of load. The amount of such power reserve would vary with time of the day depending on the incidence of different types of voltage dependent loads and also the voltage profile across the feeders. It is important for the grid operators to know the aggregate power reserve from the voltage dependent loads during different times of the day in order to schedule other forms of reserves accordingly. This paper presents a methodology to estimate such power reserve from the measured power and voltage at the bulk supply points without knowing the actual distribution network topology and/or load profile of individual customers. The proposed method is applied to estimate the time variation of the aggregate reserve offered by the voltage dependent loads within the domestic sector in Great Britain. Studies on a standard IEEE distribution network are presented to validate the estimated reserve margins under typical voltage profiles across the distribution feeders.

A Multi-Timescale Data-Driven Approach to Enhance Distribution System Observability

This paper presents a novel data-driven method that determines the daily consumption patterns of customers without smart meters (SMs) to enhance the observability of distribution systems. Using the proposed method, the daily consumption of unobserved customers is extracted from their monthly billing data based on three machine learning models: first, a spectral clustering (SC) algorithm is used to infer the typical daily load profiles of customers with SMs. Each typical daily load behavior represents a distinct class of customer behavior. In the second module, a multi-timescale learning (MTSL) model is trained to estimate the hourly consumption using monthly energy data for the customers of each class. The third stage leverages a recursive Bayesian learning (RBL) method and branch current state estimation (BCSE) residuals to estimate the daily load profiles of unobserved customers without SMs. The proposed data-driven method has been tested and verified using real utility data.

Performance investigation of grid-connected residential PV-battery system focusing on enhancing self-consumption and peak shaving in Kyushu, Japan

Abstract With increasing PV penetration level, performances of the distributed grid-connected PV system and aggregated effects on public grid need to be identified and analyzed. This paper simulated the techno-economic performances of the grid-connected residential PV-battery system based on simulated PV generations, history household load, technical and economical parameters under the electricity market in Kyushu, Japan. Results indicate that residential PV self-consumption ratios vary significantly between months, it can increase by adding relative battery size, the increasing rate shows obvious variations between months and its value highly depends on features of the customer load and PV generation profiles. Optimal management strategies for private home storage dispatch show a potential in grid peak load shaving. Assumed accumulative generation of home PV-battery system totally shares 2.0% of grid load, analysis indicates that proposed grid-supporting PV-battery systems can participate in 1.1% reduction of peak grid load. The economic feasibility and uptake of battery system highly depend on direct subsidies due to high capacity cost, payback period of PV-battery system is around 18 years without any incentive policies. Increasing electricity pricing, decreasing PV feed-in tariff and falling cost in battery can provide the home PV-battery system more attractiveness.

Investigating the Impact of Load Profile Attributes on Demand Response Exchange

This paper investigates the impact of load profiling attributes on the demand response exchange (DRX) mechanism. Modeling of demand response (DR) seller bids in traditional approach/model includes a customer willingness factor assigned in a random/arbitrary manner. The proposed model of DR bids takes into account, the load attributes such as utilization and availability factors. The criticality and willingness of the DR load/customer are embedded into seller market bids through utilization and availability factor, respectively. Various cost models have been developed to emulate the possible customer behavior in DR cost modeling. However, modeling of indistinct and uncertain nature of customer behavior is a complex issue in real-time consideration. Therefore, this paper also presents a fuzzy inference system (FIS) for considering the customer load profile attributes in DR bids. In addition, parameter tuning of fuzzy membership functions is also carried out in this paper using heuristic optimization techniques to improve the performance of FIS in DRX clearing. The proposed methodology with load profile attributes is simulated considering various load types across different load sectors. Simulation results of proposed tuned FIS-based DRX clearing are compared with other nonfuzzy models, conventional models without load attributes and untuned FIS system to demonstrate the effectiveness of tuned FIS with load profile attribute consideration in DRX clearing.

Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings

As utility electricity rates evolve, pairing solar photovoltaic (PV) systems with battery storage has potential to ensure the value proposition of residential solar by mitigating economic uncertainty. In addition to batteries, load control technologies can reshape customer load profiles to optimize PV system use. The combination of PV, energy storage, and load control provides an integrated approach to PV deployment, which we call “solar plus”. The U.S. National Renewable Energy Laboratory’s Renewable Energy Optimization (REopt) model is utilized to evaluate cost-optimal technology selection, sizing, and dispatch in residential buildings under a variety of rate structures and locations. The REopt model is extended to include a controllable or “smart” domestic hot water heater model and smart air conditioner model. We find that the solar plus approach improves end user economics across a variety of rate structures – especially those that are challenging for PV – including lower grid export rates, non-coincident time-of-use structures, and demand charges.

A New Pseudo Load Profile Determination Approach in Low Voltage Distribution Networks

Low voltage (LV) state estimation in distribution networks mainly relies on pseudo measurements because the real-time monitoring is impossible for all customers. This paper presents a new method to determine the pseudo load profiles (PLPs) of customers with small consumptions connected to the LV distribution networks. This method is comprised of two stages. First, a new frequency-based clustering algorithm is proposed to extract the essential load patterns of limited number of customers who are equipped by smart meters and are called as sample customers. The superior performance of the proposed clustering algorithm is also shown in comparison with three of the most widely used clustering methods: k-means, self-organizing maps and hierarchical algorithms. In the second stage, a new approach is proposed to estimate the daily energy consumptions two weeks ahead for other customers who are not equipped by smart meters by using their previous billing cycle energy consumptions and the load data of sample customers. The PLP of a customer is obtained by multiplying the estimated daily energy consumption by the corresponding normalized load pattern. Studies have been conducted on the data of a real distribution system to verify the proposed method and to show its application for the PLP estimation of distribution networks.

Uncertainty Modeling and Price-Based Demand Response Scheme Design in Smart Grid

Transforming conventional passive customers into active participants who interact with the utility in real time is the key idea of demand response (DR) in smart grid. However, an effective and efficient DR scheme relies on precise prediction and modeling of the uncertainties, i.e., renewable generations and load demands. In this paper, we first present a series of linear prediction models for the load prediction purpose, such as standard autoregressive (AR) process and time-varying AR (TVAR) process, according to different assumptions on the stationarity of customer load profile: piecewise stationarity, local stationarity, and cyclostationarity. Two important issues in AR/TVAR models are addressed: determining the order of AR/TVAR models and calculating the AR/TVAR coefficients. The partial autocorrelation function is analyzed to determine the model order, and the minimum mean squared error estimator is adopted to derive the AR/TVAR coefficients, which leads to the Yule–Walker type of equations. With the load prediction problem addressed, we further design a DR scheduling scheme based on utility cost minimization with different customer clustering sizes. The optimal DR load profiles are given in forms of both 1-D and 2-D water-filling solutions . A tradeoff strategy, which attempts to balance the competing objectives (centralized and distributed), is also provided based on the price-of-anarchy analysis. Simulation results of both the load prediction models and the DR schemes are presented and analyzed.

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

Harmony search optimization of renewable energy charging with energy storage system

We propose an efficient harmony search algorithm for charge scheduling of an energy storage system (ESS) with renewable power generators under time-of-use pricing and demand charge policy. We show the superiority of the proposed method by simulating experiments with load and generation profiles of typical residential customers. ESS scheduled by the proposed harmony search algorithm showed improved result over the system optimized using genetic algorithm.

A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs

Price arbitrage involves taking advantage of an electricity price difference, storing electricity during low-prices times, and selling it back to the grid during high-prices periods. This strategy can be exploited by customers in presence of dynamic pricing schemes, such as hourly electricity prices, where the customer electricity cost may vary at any hour of day, and power consumption can be managed in a more flexible and economical manner, taking advantage of the price differential. Instead of modifying their energy consumption, customers can install storage systems to reduce their electricity bill, shifting the energy consumption from on-peak to off-peak hours. This paper develops a detailed storage model linking together technical, economic and electricity market parameters. The proposed operating strategy aims to maximize the profit of the storage owner (electricity customer) under simplifying assumptions, by determining the optimal charge/discharge schedule. The model can be applied to several kinds of storages, although the simulations refer to three kinds of batteries: lead-acid, lithium-ion (Li-ion) and sodium-sulfur (NaS) batteries. Unlike literature reviews, often requiring an estimate of the end-user load profile, the proposed operation strategy is able to properly identify the battery-charging schedule, relying only on the hourly price profile, regardless of the specific facility’s consumption, thanks to some simplifying assumptions in the sizing and the operation of the battery. This could be particularly useful when the customer load profile cannot be scheduled with sufficient reliability, because of the uncertainty inherent in load forecasting. The motivation behind this research is that storage devices can help to lower the average electricity prices, increasing flexibility and fostering the integration of renewable sources into the power system.

Optimal Stochastic Tracking for Primary Frequency Control in an Interactive Smart Grid Infrastructure

An optimal stochastic tracking scheme is proposed for an interactive smart grid infrastructure made of three steps: 1) The utility reshapes the customer load profiles by scheduling a demand response for the requested customer loads; 2) individual smart home makes optimal sequential decisions on power purchase; and 3) optimal stochastic control schemes for the active power balance (primary frequency control) are designed, in the presence of uncertainties arising from customer loads and distributed renewable generations. With the first two parts addressed in our previous work, in this paper, we focus on the primary frequency control scheme design in the multilayer control architecture to stabilize frequency and maintain the active power balance within the distributed areas. We propose two stochastic tracking schemes based on the state-space representation of a synchronous generator: 1) reference-dynamics-based tracking and 2) reference-statistics-based tracking. We further extend the proposed optimal controllers by considering the realistic scenario of asynchronous load signals from different customers. To compensate for different delays seen by different customer signals, a Kalman-filter-based prediction scheme is proposed to estimate the correct reference signal. We show that the centralized reference prediction can equivalently be implemented distributively. Simulation results are presented, showing the performances of the proposed prediction and tracking schemes.