Residential Demand Profiles Research Articles

Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain.

Demand response (DR) has been studied widely in the smart grid literature, however, there is still a significant gap in approaches that address security, privacy, and robustness of settlement processes simultaneously. The need for security and robustness emerges as a vital property, as Internet of Things (IoT) devices become part of the smart grid; in the form of smart meters, home energy management systems (HEMSs), intelligent transformers, and so on. In this paper, we use energy blockchain to secure energy transactions among customers and the utility. In addition, we formulate a mixed-strategy stochastic game model to address uncertainties in DR contributions of agents and achieve optimal demand response decisions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. We use a real dataset of residential demand profiles and photovoltaic (PV) generation to validate the performance of the proposed scheme. The results show the impact of electric vehicle (EV) discharging and customer demand reduction on increasing the probability of successful block mining and improving customer profits. Moreover, the results demonstrate the security and robustness of our consensus algorithm for detecting malicious activities.

A survey-based electricity demand profiling method for developing countries: The case of urban households in Bangladesh

Demand side management (DSM) is an effective approach to reducing electricity peak demand during hours of system peak. Many different DSM schemes are being efficiently used in the developed world, based on the observed demand profiles of residences through smart meters. Although there is potential to implement DSM in developing countries, lack of smart technologies, and insufficient granular information about the residential demand profile is hampering the development of different DSM schemes. An alternate approach not involving smart technology could be crucial to solve this problem. In this study, a survey-based demand profiling method is proposed in the absence of smart meter technology, particularly for the developing world, and Bangladesh is considered as a case study. The method involves a step-by-step procedure to construct an estimated average demand profile for the household. The results show that the estimated average demand varies between 3.3% and 10% from the average measured demand during system peak hours. The median demand variation is about 1.72%. On the other hand, during off-peak hours, the minimum and maximum variations are 21% and 47%, respectively, with a median variation of 39%. Therefore, the survey-based estimated demand profile is reasonably more accurate during peak demand hours than off-peak hours, and could be used for proper DSM strategy design to reduce peak demand. This demand profiling method might be a helpful tool for national electricity authorities.

PV hosting capacity of LV distribution networks using smart inverters and storage systems: a practical margin

This study proposes an approach to evaluate a practical margin for photovoltaic (PV) generation hosting capacity (HC) of low voltage distribution networks. This HC is determined considering the randomness of the connection points and is supposed to be the maximum value of PV penetration up to which the utility can authorise any interconnection without performing additional case studies. Smart inverter control strategies and battery storage systems are used to avoid costly network expansion solutions. The simulations are performed using actual solar radiation data and residential demand profiles. The results show an increase in the network HC, bringing benefits by deferring network investments such as conductors and asset upgrades.

Experimental determination of ZIP coefficients for residential appliances and ZIP model based appliance identification: The case of YTU Smart Home

This study presents the experimental determination of the ZIP (constant impedance (Z), constant current (I), constant power (P)) coefficients for residential appliances in a smart home at Yildiz Technical University (the YTU Smart Home) in Istanbul, Turkey. First, active and reactive power consumptions of house appliances are measured with respect to the voltage change between 100–240V with a 10V increment. Then, Least Square Algorithm is used to calculate ZIP coefficients of household appliances. Using the obtained ZIP coefficients and an assumed appliance usage pattern, a daily residential active and reactive power demand profiles are obtained. Data from the ZIP models are compared with actual measurement data. It is shown that the ZIP models are quite accurate within 200–220V voltage range and can be used in generating residential demand profiles for system level analyses. In addition, the ZIP model based appliance identification algorithm is proposed to identify the plugged appliance in the smart home environment for better home energy management algorithm implementation.

Optimal energy management in all-electric residential energy systems with heat and electricity storage

Residential demand profiles typically demonstrate a mismatch between energy demand and PV supply. Different solutions are proposed, such as demand side management and energy storage systems. Nevertheless, costs and environmental impacts of some technologies (e.g. batteries) are high. This paper proposes two system designs: Home Energy Storage (HES) and Community Energy Storage (CES). Besides electricity storage, heat storage is used in the two system designs to supply domestic hot water and space heating. Furthermore, the trade-offs between the different storage mediums in relation to costs are analyzed. To achieve that, different methodologies are used to size the electricity and heat storage mediums for HES and CES. Next, a multi-objective mixed integer linear programming model is developed to optimize the operation costs and CO2-emissions for each system design. After that, the model is tested on a residential community situated in Cernier (Switzerland). The results demonstrate that CES performs better than HES on economic and environmental performance due to economies of scale and the optimally sized storage capacity of the battery in CES. Currently, none of the proposed system designs is economically feasible. However, the sensitivity analysis shows that a profitable system design can be obtained for both HES and CES, when the electricity storage (i.e. battery storage) size is reduced and the heat storage (i.e. water storage tank) size is increased.

Impacts of microgrids with renewables on secondary distribution networks

High penetration of distributed generation (DG) in secondary (low-voltage) distribution networks (SDN) is one of the primary challenges of network operators. Particularly in radial LV networks, renewable intermittency and bidirectional power flow may cause critical problems such as under/over voltages, line overloads and high energy losses. System operators usually consider the worst-case scenarios for the assessment of the impacts of new integrations and usually they prefer passive methods to mitigate the negative effects. However, analysis of daily operation with high resolution, stochastic DG supply and residential demand profiles can reveal both the occurrence rates and the severities of the critical events, while paving the way for the development of active mitigation methods. This study is focused on detailed daily operation analysis of SDNs with renewable-based microgrids. Seasonal DG output and residential demand profiles throughout a day were simulated using a virtual test bed to investigate the critical cases.

Clustering analysis of residential electricity demand profiles

Little is known about variations in electricity use at finely-resolved timescales, or the drivers for those variations. Using measured electricity use data from 103 homes in Austin, TX, this analysis sought to (1) determine the shape of seasonally-resolved residential demand profiles, (2) determine the optimal number of normalized representative residential electricity use profiles within each season, and (3) draw correlations to the different profiles based on survey data from the occupants of the 103 homes. Within each season, homes with similar hourly electricity use patterns were clustered into groups using the k-means clustering algorithm. Then probit regression was performed to determine if homeowner survey responses could serve as predictors for the clustering results. This analysis found that Austin homes fall into one of two seasonal groups with some homes using more expensive electricity (from a wholesale electricity market perspective) than others. Regression results indicate that variables such as if someone works from home, hours of television watched per week, and education levels have significant correlations with average profile shape, but might vary across seasons. The results herein also indicate that policies such as time-of-use or real-time electricity structures might be more likely to affect lower income households during some high electricity use parts of the year.

An Integrated Tool for Assessing the Demand Profile Flexibility

The purpose of this paper is to describe an integrated tool whose aim is to assess the residential demand profile flexibility through the control of space conditioning loads, mainly air conditioner and heat pump appliances. This assessment has been divided into two principal tasks - an estimation of the controllable load and a selection of the optimum load control strategy according to a target profile and a set of prefixed constraints. The tool also provides the aggregated load behavior, allowing a comparison between different load strategies. This tool can be applied from the customer's and the utility's side. An application example to a real environment is also presented.