Duong Tung Nguyen

Joint Optimization of Electric Vehicle and Home Energy Scheduling Considering User Comfort Preference

In this paper, we investigate the joint optimization of electric vehicle (EV) and home energy scheduling. Our objective is to minimize the total electricity cost while considering user comfort preference. We take both household occupancy and EV travel patterns into account. The novel contributions of this paper lie in the exploitation of EVs as dynamic storage facility as well as detailed modeling of user comfort preference, thermal dynamics, EV travel, and customer occupancy patterns in a concrete optimization framework. Extensive numerical results are presented to illustrate the efficacy of the proposed design. Specifically, we show that the proposed design can achieve significant saving in electricity cost, allow more flexibility in setting the tradeoff between cost and user comfort, and enable to reduce energy demand during peak hours. We also demonstrate the benefits of applying the proposed framework to a residential community compared to optimization of individual household separately.

Optimal energy management for cooperative microgrids with renewable energy resources

In this paper, we present an optimal energy management framework for a cooperative network of heterogeneous microgrids (MGs) where energy exchange among connected MGs is allowed to exploit the fluctuations of stochastic energy sources and demands. A multi-objective function is introduced that seeks to achieve an efficient tradeoff between low operation cost and good energy service for customers. The objective function captures the total cost of power exchange with the main grid, the startup and shutdown costs, the operating cost of distributed generators (DGs), the payment for demand response load, the penalty costs for involuntary load curtailment, and renewable energy spillage. We propose to employ the scenario-based two-stage stochastic optimization approach to deal with the uncertainties of renewable energy resources and load demand in the energy scheduling problem. The efficacy of the proposed energy management solution is demonstrated via numerical results.

Energy Management for Households With Solar Assisted Thermal Load Considering Renewable Energy and Price Uncertainty

In this paper, we investigate the energy scheduling problem for a household equipped with a solar assisted heating, ventilation, and air conditioning, and water heating system in the real-time pricing environment. Our objective is to minimize the electricity cost while maintaining users thermal comfort requirements. We consider different types of loads with different characteristics, detailed modeling of thermal dynamics, and uncertainty in electricity price and solar energy. The advantage of the proposed design lies in the exploitation of the solar assisted thermal system that can flexibly utilize the energy from the solar source or from the grid during low-price periods to support the home hot water demand, user thermal indoor temperature preference while reducing the electricity cost. We present numerical results to illustrate the effectiveness of our proposed design. Specifically, we show that the proposed design can achieve significant cost saving, allow flexible tradeoff between user comfort tolerance and electricity cost reduction, and efficiently adjust the electricity consumption load profile. We also analyze the influence of solar assisted thermal system factors such as the water tank temperature limit, solar collector size, and weather condition on the achievable cost.

Dynamic Pricing Design for Demand Response Integration in Power Distribution Networks

This paper presents optimal pricing design for demand response (DR) integration in the distribution network. In particular, we study the energy scheduling problem for a load serving entity (LSE) that serves two types of loads, namely inflexible and flexible loads. Inflexible loads are charged under a regular pricing tariff while flexible loads enjoy a dynamic pricing tariff that ensures cost saving for them. Moreover, flexible loads are assumed to be aggregated by several DR aggregators. The interaction between the LSE and its customers is formulated as a bilevel optimization problem where the LSE is the leader and DR aggregators are the followers. The optimal solution of this problem corresponds to the optimal pricing tariff for flexible loads. The key advantage of the proposed model is that it can be readily implemented thanks to its compatibility with existing pricing structures in the retail market. Extensive numerical results show that the proposed approach provides a win-win solution for both the LSE and its customers.

Home energy management with generic thermal dynamics and user temperature preference

In this paper, we develop a novel home energy management solution that aims to minimize the electricity cost and guarantee user comfort in terms of preferred home temperature. Specifically, we consider a typical household system with a Heat Ventilation and Air Conditioning (HVAC) system and various types of loads. We formulate the home energy scheduling problem considering generic thermal dynamics represented by a look-up table, thermal comfort constraints, and specific characteristics of different electric loads. The assumed generic thermal dynamics overcome limitations of other approximate equation-based thermal dynamics typically employed in the literature. However, the empirical thermal dynamics makes the energy scheduling problem a complicated non-linear optimization problem, which is difficult to tackle. Therefore, we develop a decomposed solution approach where the scheduling of HVAC system and other loads are optimized in two different steps. We show that the HVAC scheduling problem is a dynamic programming problem and develop an algorithm to find its optimal solution considering the user comfort constraint Given the optimal HVAC scheduling solution, the scheduling problem for remaining loads is transformed into a mixed integer program whose solution can be found by using an available optimization solver. We then present numerical results to demonstrate the effectiveness and correctness of our proposed solution and its relative performance compared with the conventional design.

Optimal energy trading for building microgrid with electric vehicles and renewable energy resources

In this paper, we study an optimal power bidding and scheduling problem for a microgrid (MG), which consists of distributed generators (DGs), battery storage units, a large garage with many charging stations for electric vehicles (EVs), MG local load, and renewable energy sources (RESs).We propose to utilize EVs as a dynamic energy storage facility to accommodate the variability of RESs in a realistic economic model for the electricity market. The power scheduling and bidding problem is formulated as a two-stage stochastic programming problem considering the uncertainties of RESs and electricity price. Specifically, a multi-objective function is introduced to balance the tradeoff between maximizing the MG revenue and minimizing the MG operating cost. Importantly, appropriate penalty metrics capturing involuntary load shedding, renewable energy curtailment, and bid deviation are integrated into the objective function. Numerical results confirm the effectiveness of the proposed optimization framework in enhancing the operation efficiency of the MG, reducing curtailment of renewable energy resources compared to the conventional scheme and flexibility of the proposed framework in balancing different design objectives.