Optimal Planning and Operation of Distributed Energy Resources

In the present scenario, the power system expansion has been elevated due to the growing demand of energy with increase in population. The modernization of society is also the key factor for increase in energy need. This chapter discusses the installed capacity for both renewable and non-renewable energy resources in India. Further, this energy need can only be mitigated by the integration of renewable energy sources (RES) as DG source in the existing distribution network. Also, RES integration in the distribution network will reduce the pollution level. This chapter includes the global CO₂ emission trend since 2000. The current need of the installation of new power plant can also be mitigated by the installation of DG sources near the load point. This chapter includes the basic of DG and its technical, economical and environmental aspects.

Decentralization of the power sector is currently gaining attention by power system planners to satisfy the growing energy demand of the society. Due to the deterioration of the worldwide environment and the continued depletion of global energy, renewable energy resources have gotten a lot of attention in recent years due to their self-renewal nature over time. The integration of renewable energy (RE) resources in existing power system suppress the need for new generation infrastructure for growing population. Distributed energy resources (DER) are referred as electricity generation by small generating units like solar photovoltaics, windmills, biomass, tidal wave, etc. installed near the load centers or installed at distribution grid. The integration of RE-based DG in distribution network is currently gaining more attention among transmission system operators (TSOs) as it fulfills the end user demands for reliable, economic and pollution-free energy source. Although most of the DER are renewable in nature, the output is intermittent in nature. The optimal siting and sizing of DER in distribution network for power loss minimization, voltage profile improvement and reliability enhancement are another aspect of DG integration. Still, DG integration can improve the system performance. This book chapter is confined to the review of existing distributed generation technologies, their potential capabilities to satisfy energy demand, types, basic operation, various aspects with DG penetration and relative merits and demerits.

This chapter presents the design of efficient controllers and protection systems for distributed energy resources (DERs)-based microgrids. The control strategies for DERs include decentralized, centralized, and hierarchical controllers. These controllers have been designed based on a robust extended linear quadratic Gaussian (LQG) control, which combines the Kalman estimator with the linear quadratic regulator with prescribed degree of stability (LQRPDS). Finally, this chapter demonstrates the validation of the design procedure of the DER controllers using eigenvalue analysis, offline time-domain simulations, and RTDS-based simulations. Moreover, various protection systems for DERs-based microgrids have been discussed briefly.

A strategy for managing active distribution systems (ADS) requires the use of optimal control techniques that find a good solution to reduce active power losses. The solution to this type of problem presents two major difficulties: on the one hand, the optimisation problem is generically formulated as a mixed-integer nonlinear propagation problem, whose solution requires a high computational cost and, on the other hand, its application to complex distribution networks, which are characterised by their radial nature, large number of busbars, long length, and significant imbalances due to the presence of loads distributed unevenly between the phases. This chapter presents the development of an economic dispatch (ED) optimisation model for active distribution system management (ADSM). In this context, DIgSILENT Power Factory^® provides useful tools to simulate complex systems. On the one hand, the DIgSILENT Programming Language (DPL) can be used for multiple purposes such as automation of simulations, automatic scenario generation, analysis of results, etc. On the other hand, DIgSILENT Power Factory^® supports some external interfaces that can be used for data exchange and coupling with MATLAB^®. The control and optimisation strategy are validated in IEEE 34-Node Distribution Test Feeder.

An optimal siting and sizing of renewable energy sources (RES) play a substantial role in achieving the efficient operation and planning of the distribution system. It majorly affects the active power loss and voltage stability of the system. Therefore, this study formulates a multi-objective function that optimally allocates and sizes the RES in the distribution network by minimizing the total active power losses (TAPL) and voltage deviation (VD) of the network buses. The proposed algorithm is validated on an IEEE 69-bus model having two types of loads, i.e. consumer load and charging load of an electric vehicle. The consumer load is modelled as a polynomial type of load, and a total of four electric vehicle charging stations (EVSs) are integrated into the model. The formulated objective function also optimally allocates these four EVSs to minimize the TAPL and VD of the system. The objective function is minimized using four different optimization techniques, i.e. genetic algorithm (GA), grey wolf optimizer (GWO), particle swarm optimization (PSO), and hybrid particle swarm grey wolf optimization (HPSGWO). The simulation studies showed that the HPSGWO is superior to all other algorithms as it provides the solution having the lowest TAPL and VD in the system.

The electric vehicle (EV) market has seen remarkable growth in recent years. The number of EVs in the market is increasing day-by-day, demanding more energy in the power distribution system (PDS). So, it is of great significance to closely monitor and optimize every aspect related to EV’s charging–discharging (CD) planning. Controlled or coordinated and uncontrolled or uncoordinated CD scheduling are two types of techniques by which proper CD scheduling pattern is channelized for EVs. Without a well-coordinated schedule and a well-planned strategy for charging EVs, users (individual or parking lot owners/operators (PLO)) will typically apply an immediate charging, which burdens the system, increases the chance for grid instability, affect the voltages profile, produces harmonics, frequency deviations, increases the cost of charging, degrades vehicles battery life, damages charging station infrastructures efficiency, etc. By maintaining a CD equilibrium plan for EVs, various adverse effects of uncoordinated scheduling can be avoided, which are thoroughly discussed in this paper along with the challenges and issues faced by EV applications from the aggregators as well as customers’ point of view, curve shaping, PLO benefits while satisfying EV owners’ charging requirements. Along with these, this paper contributes toward the recently implemented scheduling methods which are helpful in selecting the charging points for EVs that arrive at parking stations to accomplish several objectives.

Accurate prediction of electricity demand and renewable generation such as solar and wind power is required for secure and reliable operation of power systems. However, prediction of uncertain generation and demand parameters is formidable challenging task, and the accuracy of forecasting models is being continuously ameliorated. The prediction error significantly affects the system operation if wind and solar generation penetration are high. Hence, the characterization of prediction error is imperative for the accuracy of the decision-making problems like unit commitment and economic dispatch. This chapter provides a detailed overview of forecasting techniques and simultaneously investigates the effect of prediction error on power system operation through security-constrained unit commitment (SCUC) problem. The SCUC problem with wind generation, solar generation and generic battery energy storage system is modelled through two-stage stochastic programming approach. In the first stage, day-ahead SCUC problem is solved for point forecasts of uncertain parameters while in the second stage real-time SCUC is solved considering wind and solar power uncertainty. Uncertainty is modelled through probabilistic scenarios. The considered SCUC problem is illustrated through practical case studies based on modified IEEE 39 bus system. Numerical results obtained show that high error in forecasting would increase the balancing cost, wind and solar power spillage and load shedding. However, power spillage and shedding can be minimized significantly by the optimal utilization of energy storages.

The usage of renewable energy sources (RES) with energy storage like battery for standalone household and commercial purposes is becoming popular. These systems have to manage the given amount of generated energy from the RES. In this chapter, an efficient hybrid standalone system with its power management strategy has been developed. The proposed configuration is implemented to deal with the intermittent nature of the energy generated by the photovoltaic (PV) array and processing its power through a parallel combination of two converters. The two converters consist of a DC-DC boost converter and a buck converter. Apart from processing the PV power, the battery power is processed through a DC-DC full-bridge converter to maintain the required load voltage. A load voltage-based power management scheme along with the maximum power extraction from the PV source is also proposed in this chapter. The proposed power management scheme has been verified with both simulation and experimental results. An experimental prototype with a capacity of 150 watts is also made to show the different modes of operation of the proposed system and its smooth transition between various modes.

The sheer volume of real-time data related to grid security, power quality, energy price, energy demand, etc., is the main challenge in smart grid. Integration of renewable energy has introduced more uncertainty in the grid. Hence, real-time prediction, analysis, and control of smart grid are difficult. As machine learning algorithms are capable to efficiently process data and find the hidden relationships among variables, the focus lies on utilizing the machine learning techniques in smart grid applications to predict the decisions, even in uncertain conditions. This chapter covers some important machine learning techniques. Further, how to use machine learning techniques in power system security analysis, calculation of available transfer capability of tie lines, and forecasting of electricity price/load are presented.

In this chapter, mechanism for energy pool management is discussed. In the following sections of the chapter, the benefits and challenges of distributed energy resources (DER) are presented in detail. Further, the need of optimal sizing and energy management of energy pool is elaborated. Moreover, a case study on base station of telecom tower is carried out to demonstrate the impact of energy management on hybrid energy pool. Firstly, the optimal sizing of the system is implemented. A multi-objective optimal sizing problem is formulated to estimate the size of PV panels and battery bank. A macrocell telecom tower base station is considered having peak load of 3.5 kW. Three objectives are formulated which are: annualized cost of electricity (LCOE), loss of power supply probability (LPSP), and excess energy generation (EE). Further, a VES-based DRM is implemented, and the performance of the system is evaluated based on the performance index defined. A VES-based DRM algorithm is implemented on same random day which align the turn on and off of air conditioner with respect to availability of PV generation. Moreover, the comparative analysis is carried out to validate the effectiveness of VES-based DRM over without VES-based DRM.

Due to the increased demand for energy and depletion of the fossil fuels, the energy production using renewable energy resources simultaneously with the conventional power generating plant will make a significant contribution to sustainable power production. Wind and solar energy resources are the promising sources of renewable energy. They have huge potential that can satisfy the energy demand and can reduce the emission of harmful greenhouse gases emitted by the conventional power plants. Reliability assessment is one of the key indicators to measure the impact of the renewable energy-based distributed generation (DG) units in the distribution networks and to minimize the cost that is associated with power outage. Being the only link between consumers and utility, it becomes the prime need to enhance the reliability of distribution network in terms of reduction in expected interruption cost and energy not served. This chapter presents the reliability assessment of distribution network connected at Bus-2 of Roy Billinton Test System (RBTS) with and without DG. To get an in-depth assessment on DG impact, multiple DG placement is also considered in this chapter. The results obtained from the case studies have demonstrated the effectiveness of using DG to enhance the reliability of the conventional distribution system.