Duong Quoc Hung

Analytical Expressions for DG Allocation in Primary Distribution Networks

This paper proposes analytical expressions for finding optimal size and power factor of four types of distributed generation (DG) units. DG units are sized to achieve the highest loss reduction in distribution networks. The proposed analytical expressions are based on an improvement to the method that was limited to DG type, which is capable of delivering real power only. Three other types, e.g., DG capable of delivering both real and reactive power, DG capable of delivering real power and absorbing reactive power, and DG capable of delivering reactive power only, can also be identified with their optimal size and location using the proposed method. The method has been tested in three test distribution systems with varying size and complexity and validated using exhaustive method. Results show that the proposed method requires less computation, but can lead optimal solution as verified by the exhaustive load flow method.

Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction

This paper investigates the problem of multiple distributed generator (DG units) placement to achieve a high loss reduction in large-scale primary distribution networks. An improved analytical (IA) method is proposed in this paper. This method is based on IA expressions to calculate the optimal size of four different DG types and a methodology to identify the best location for DG allocation. A technique to get the optimal power factor is presented for DG capable of delivering real and reactive power. Moreover, loss sensitivity factor (LSF) and exhaustive load flow (ELF) methods are also introduced. IA method was tested and validated on three distribution test systems with varying sizes and complexity. Results show that IA method is effective as compared with LSF and ELF solutions. Some interesting results are also discussed in this paper.

Determining PV Penetration for Distribution Systems With Time-Varying Load Models

A constant or voltage-dependent load model is usually assumed in most distributed generation (DG) planning studies. However, this paper proposes several different types of time-varying voltage-dependent load models to determine the penetration level of photovoltaic (PV) units in a distribution network. Here, a new analytical expression is first proposed to size a PV unit, which can supply active and reactive powers. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of three indices, namely active power loss, reactive power loss and voltage deviation. The expression is then adapted to allocate PV units while considering the time-varying load models and probabilistic PV generation. The effectiveness of the proposed approach was validated on 69- and 33-bus test distribution systems. The results showed that PV allocation with different types of time-varying load models can produce dissimilar penetration levels.

DG Allocation in Primary Distribution Systems Considering Loss Reduction

Loss reduction in distribution systems has been a subject of great concern since the evolution of the interconnected power system. In the recent past, with increasing interest in climate change and energy security, renewable energy integration and energy efficiency, including loss reduction, have been considered as twin-pillars of sustainable energy solutions. When renewable energy is integrated by considering loss reduction as an additional goal, it would lead to multi-fold benefits. This chapter presents the application of distributed generation for loss reduction. The two key issues of the most suitable location and appropriate size of distributed generation for loss reduction have been discussed. Analytical expressions have been developed for finding the appropriate size of different types of distributed generations. Methodologies are presented for locating the DG in primary distribution feeders, assuming primary energy resources are evenly distributed along the feeder. The analytical expressions and placement methodologies have been tested in three test distribution systems of varying sizes and complexity.

A Day-Ahead Forecasting Model for Probabilistic EV Charging Loads at Business Premises

Focusing on every individual electric vehicle (EV) while optimally charging a significant number of EV units at the workplace is normally computationally burdensome. Such charging optimization requires not only a long runtime but also a large CPU memory due to numerous decision variables involved. This paper develops a new combined state of charge (SOC) based methodology to calculate day-ahead combined probabilistic charging loads for a large number of EV units. Here, several models are proposed to estimate different combined statistical parameters based on historical data. The proposed methodology determines the transition of the combined SOC distribution of EV units from one timeslot to the next using these estimated parameters. Various strategies of SOC-based charging (e.g., unfair and fair modes) are investigated to control EV loads. Numerical results show that the proposed SOC-based charging can reduce the number of decision variables significantly, and require less computational time and memory accordingly.

Assessing the impact of loss reduction on distributed generation investment decisions

This paper proposes a methodology for evaluating the impact of loss reduction on DG investment decisions. In this methodology, new analytical expressions are first proposed to quickly capture the optimal power factor of each DG unit to maximize loss reduction. The decision for the optimal location, size and number of DG units is then obtained through a benefit-cost analysis over a given planning horizon. Here, the total benefit includes energy sales and additional benefits, namely loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology has been applied to a 69-bus test distribution system. The results show that the additional benefits including loss reduction are imperative. Inclusion of these in the analysis would result in faster DG investment recovery.

Technical challenges, security and risk in grid integration of renewable energy

Renewable energy sources, especially wind and solar photovoltaic (PV) are well on the way to dominate power systems as their penetration levels are increasing at a staggering rate in many countries. Consequently, utilities around the world are discussing and conducting feasibility studies of “100 %” renewable energy based power systems. Even though a very high penetration level of renewable energy looks plausible, the technical challenges associated with them could be a hindrance to the seamless integration. This chapter focuses on technical challenges associated with the grid integration of renewable energy and they are classified as challenges in the distribution and transmission systems, respectively. Most of the PV integration is happening in the distribution system while wind integration can also be in the transmission system. This chapter also highlights security concerns and associated risks, which are categorized as technical and non-technical issues. Finally, the chapter summarizes the way forward for the renewable energy integration in power systems.

A loss sensitivity factor method for locating ES in a distribution system with PV units

This paper presented an approach to place single and multiple Energy Storage (ES) units in a distribution network with photovoltaic (PV) units considering energy losses. The approach is based on ALSFmax, which is defined as a difference between the minimum and maximum Loss Sensitivity Factor (LSF) values. The GridLAB-D simulation software is used to model test systems under the study and to validate the proposed method. Results show that the proposed ALSFmax approach can yield lower energy losses and better voltage profiles than other methods: average LSF, centralized ES (i.e. ES is located at the substation) and ES located near the PV units. Some interesting results are also presented in the paper.