Zhanle Wang

Residential demand response: An overview of recent simulation and modeling applications

This paper reviews recent simulation and modeling applications of residential demand response including demand response enabled load models, home energy management systems, and multi-agent systems. Demand response implementation in residential sectors is a recent effort to improve efficiency of the electricity market and stability of the power system. The benefits are significant; however the investment and potential risks are nonnegligible. Simulation and modeling is a desirable way to identify and quantify impacts and benefits of demand response applications. The two main aims of these applications are to reduce electricity peak demand and to match the demand with renewable energy. The flexible demand aspect enables time-shift electricity consumption by bringing forward or delaying the use of appliances. Therefore, developing applicable residential load models and efficiency home energy management systems are critical issues to allow incorporation of dynamic electric use patterns. Multi-agent systems allows evaluating various components of further power system or smart grid including distributed generator, microgrid, distribution intelligence, etc..

Optimal Residential Demand Response for Multiple Heterogeneous Homes With Real-Time Price Prediction in a Multiagent Framework

Demand response (DR) is a recent effort to improve the efficiency of the electricity market and the stability of the power system. A successful implementation relies on both appropriate policy design and enabling technology. This paper presents a multiagent system to evaluate optimal residential DR implementation in a distribution network, in which the main stakeholders are modeled by heterogeneous home agents (HAs) and a retailer agent (RA). The HA is able to predict and control electricity load demand. A real-time price prediction model is developed for the HA and the RA. The optimal control of electricity consumption is formulated into a convex programming problem to minimize electricity payment and waiting time under real-time pricing. Simulation results show that the peak-to-average power ratio and electricity payments are significantly reduced using the proposed algorithms. The HA, with the proposed optimal control algorithms, can be embedded into a home energy management system to make intelligent decisions on behalf of homeowners responding to DR policies. The proposed agent system can be utilized to evaluate various strategies and emerging technologies that enable the implementation of DR.

An Evaluation of Electric Vehicle Penetration under Demand Response in a Multi-Agent Based Simulation

This paper proposes an electric vehicle charging model and various control algorithms that are further incorporated into a multi-agent system to evaluate impacts of electric vehicle penetration on the power system. Electric vehicles have become increasingly popular due to the high costs of the operation of gas / diesel powered vehicles and the potential to reduce CO2 emission. In this work, we propose the electric vehicle charging model and associated control algorithms to aggregate the electric vehicle load. Simulation results show that uncontrolled charging of electric vehicles can jeopardize the stability of the power system. In a worst-case scenario this can lead to an increase of peak demand by 53.2%, while by using appropriate scheduled charging the electric vehicles can have no contribution to the peak demand. Furthermore, scheduled charging dramatically reduces the standard deviation of the residential load (by up to 51%). Therefore, the aggregation of electric vehicle demand under an appropriate demand response control strategy has the potential to dramatically improve the stability of the power system with virtually no negative impacts. The proposed electric vehicle charging model and the associated scheduling algorithm can be embedded into a home energy management system or a smart charger.

Agent-based simulation of home energy management system in residential demand response

This paper presents an agent-base model to evaluate the home energy management system in residential demand response implementation. Residential demand response aims to change peoples electricity consumption patterns to reduce the peak demand and therefore improve energy efficiency and power system stability. The home energy management system intelligently controls household loads with association of smart meters. It plays key roles in a success demand response implementation. In the proposed agent-based model, the main stakeholders are modelled by the software agents including Conventional Home Agents, Smart Home Agents, a Utility Agent, a Primary Plant Agent and Secondary Plant Agents. A mechanism of dynamic pricing is applied to both the Conventional Home Agent System (Scenario #1) and the Smart Home Agent System (Scenario #2). Comparing to the Scenario #1, the peak demand, average householders bills and generation cost in the Scenario #2 is decreased by 24.6%, 7.4% and 14.7% respectively. This demonstrates the effectiveness of the home energy management system in the residential demand response implementation. The proposed model can be a test-bed to evaluate various demand response strategies and technologies.

Evaluating self-monitoring blood glucose strategies using a diabetic-patient software agent

We propose the method of calculating the cross-correlation between continuous blood glucose and interpolated values of blood glucose samples in order to evaluate blood glucose monitoring frequency in a diabetic-patient software-agent model. The patient software-agent model is a 24-hr circadian, self-aware, stochastic model of a diabetic patients blood glucose levels in a mobile agent environment. Monitoring frequency can vary from six times per day to as little as one time per week. Today, the cost of monitoring diabetes in the population is almost equal to the cost of its treatment. Due to the cost, discomfort and potential of infection related to blood glucose monitoring, human diabetic patients are looking for the optimal monitoring frequency which will provide good understanding of blood glucose levels but will keep blood sampling to a minimum. The proposed method quantitatively assesses various monitoring protocols regarding the monitoring frequency in nine predefined categories (health status and age) of patient agents.

Optimal scheduling algorithm for charging electric vehicle in a residential sector under demand response

This paper proposes an electric vehicle charging model and an optimal control algorithm to predict and evaluate impacts of electric vehicle penetration on the power system. Electric vehicles have become increasingly popular due to their highly efficient use of energy and their potential to reduce CO2 emissions. The proposed electric vehicle charging model simulates an individual electric vehicles load profile by capturing various characteristics of a Lithium-Ion battery such as charging demand, the state of charge and potential driving patterns. The optimal control algorithm of scheduling electric vehicle charging is formulated as a convex optimization problem under real-time pricing to minimize the electricity payments of the user. Simulation results show that uncontrolled electric vehicle charging can jeopardize the stability of the power system while scheduled charging has no contribution to the peak demand. Furthermore, scheduled charging dramatically reduces the peak to average power ratio and electricity payment of users. The proposed electric vehicle charging model can be used to study charging patterns in a simulation environment and the optimal control algorithm can be embedded into a home energy management system or a smart charger.

A signal processing application for evaluating self-monitoring blood glucose strategies in a software agent model

We propose the signal processing technique of calculating a cross-correlation function and an average deviation between the continuous blood glucose and the interpolation of limited blood glucose samples to evaluate blood glucose monitoring frequency in a self-aware patient software agent model. The diabetic patient software agent model [1] is a 24-h circadian, self-aware, stochastic model of a diabetic patients blood glucose levels in a software agent environment. The purpose of this work is to apply a signal processing technique to assist patients and physicians in understanding the extent of a patients illness using a limited number of blood glucose samples. A second purpose of this work is to determine an appropriate blood glucose monitoring frequency in order to have a minimum number of samples taken that still provide a good understanding of the patients blood glucose levels. For society in general, the monitoring cost of diabetes is an extremely important issue, and these costs can vary tremendously depending on monitoring approaches and monitoring frequencies. Due to the cost and discomfort associated with blood glucose monitoring, today, patients expect monitoring frequencies specific to their health profile. The proposed method quantitatively assesses various monitoring protocols (from 6 times per day to 1 time per week) in nine predefined categories of patient agents in terms of risk factors of health status and age. Simulation results show that sampling 6 times per day is excessive, and not necessary for understanding the dynamics of the continuous signal in the experiments. In addition, patient agents in certain conditions only need to sample their blood glucose 1 time per week to have a good understanding of the characteristics of their blood glucose. Finally, an evaluation scenario is developed to visualize this concept, in which appropriate monitoring frequencies are shown based on the particular conditions of patient agents. This base line can assist people in determining an appropriate monitoring frequency based on their personal health profile.

Agent-Based Modeling and Simulation

Computer simulation, or just simulation, is a decision support technique that enables stakeholders to conduct experiments with models that represent real-world systems of interest.

Patient-Physician Interaction Model

This work integrates Wu’s (Sycara AI magazine 19:79, 1998 [1]) model with the agent technology to develop the diabetic Patient Agent model (Martens and Benedicenti EEMA TRLabs execution environment for mobile agents, 2001 [2].

The Diabetic Patient Agent

This chapter presents and discusses the results obtained from the actual simulation of the proposed model. Various simulations have been performed to evaluate and examine the behaviour of the diabetic patient and the characteristics of the healthcare system under different scenarios. All scenarios in this section are simulated for 1 year with a fixed cost of physician and hospital visits. 5.1 Manipulating the Frequency and the Period of Self-monitoring This simulation examines the characteristics of a patient’s blood sugar and the financial cost associated with the healthcare system by modifying the frequency and the period of self-monitoring of blood glucose by the patient with an extremely unhealthy diet regimen. The parameter settings for this simulation are as follows: • Food: The patient consumes a high to very high amount of carbohydrates at breakfast, and a consistently very high amount of carbohydrates at lunch and dinner. • Physical Activity: The level of the patient’s physical activity is low to medium during the morning, afternoon and evening. • Mealtime: The patient eats breakfast around 6:00 am, lunch between 11 am and 12:40 pm, and dinner between 5:10 pm and 6:20 pm. • Medication: The patient is initially not on any medication. • Willingness to adopt healthier lifestyle: The patient’s behaviour for this parameter is set to 0%.