Hieu Trung Nguyen

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 management for building microgrid with constrained renewable energy utilization

In this paper, we consider the energy management for a building microgrid considering a probabilistic constraint on the renewable energy utilization. Facilitated by the microgrid technology with integrated renewable energy resources, we assume that the building microgrid can participate in the electricity market to efficiently utilize the renewable energy and reduce electricity cost. In this paper, we develop an optimal energy management framework for the building microgrid considering various building loads, renewable energy, storage facility, and a natural gas combined heat and power (CHP) system. In addition, we employ the chance constrained and two-stage stochastic programming approach in our design to ensure efficient utilization of the renewable energy and to capture various system uncertainties. The proposed solution addresses the risk that available renewable energy may not be fully utilized due to its intermittent nature. Extensive numerical results are presented to illustrate the effectiveness of our proposed design.

Bi-Objective-Based Cost Allocation for Cooperative Demand-Side Resource Aggregators

This paper presents a cooperative game theoretic approach to tackle the cost allocation problem for a virtual power plant which consists of multiple demand-side resource aggregators (DRAs) participating in the short-term two settlement electricity market. Given the considered game is balanced, we propose to employ the cooperative game theory’s core cost allocation concept to efficiently allocate the bidding cost to the DRAs. Since the nonempty core contains many potential solutions, we develop a bi-objective optimization framework to determine the core cost allocation solution that can achieve efficient tradeoff between stability and fairness. To solve this problem, we jointly employ the

Online ensemble learning for security assessment in PMU based power system

{\epsilon }

Coordinated dispatch of renewable energy sources and HVAC load using stochastic programming

-constraint and row constraint generation methods to construct the Pareto front, based on which we can specify a desired operation point with reasonable computation effort. Numerical studies show that our proposed design can efficiently exploit the nonempty core to find a cost allocation for the participants, achieve the desirable tradeoff between stability and fairness, and can address the practical DRAs’ large-scale cooperation design.

Sharing Profit From Joint Offering of a Group of Wind Power Producers in Day Ahead Markets

This paper presents an online Adaboost algorithm for ensemble of support vector machines (SVM) for power system security assessment utilizing online measurement data obtained from Phasor Measurement Units (PMUs). Our proposed learning scheme consists of a strong learner and multiple weak learners. The weak learners are linear SVMs which are easy to implement and incrementally updated with low computation complexity. The strong learner compensates for inevitable classification errors of linear SVMs by using the boosting approach. Since the data is unbalanced, i.e., the number unsecured scenarios is much smaller than the number of secured scenarios, conventional online Adaboost may result in the high misdetection rate. Hence, we propose an online Adaboost algorithm that can adapt itself to the unbalanced online data. In addition, efficient tradeoff between misdetection (i.e., failing to detect unsecured samples) and false alarm (i.e., classifying secured samples wrongly) can be achieved by tuning a design parameter. Numerical results show that our proposed scheme can achieve high security assessment efficiency and accuracy, which is potential for the advanced security monitoring application in future smartgrid.