The proliferation of intermittent renewable-energy-based power systems and the emergence of new types of loads are likely to introduce new powe-quality and power-demand management challenges in a smart grid. An additional level of complication gets added when the system deals with a mass penetration of uncontrolled mobile energy sources and loads, that is, electric vehicles (EVs), to the grid. However, the use of an advanced EV management technique can overcome the challenges through an intelligent bidirectional energy transfer process. This chapter highlights various control and optimization techniques to manage the power demand of both single and multiple customers in a smart grid using EVs. The techniques cover both the energy resource and load-management approaches. Energy-resource management technique for single customer coordinates between EV, photovoltaics (PV) and battery storages based on the peak and off-peak load conditions, to minimize the peak load and electricity cost with an increased efficiency. Likewise, the energy-resource management for multiple customers, controls the aggregated PVs, battery storage and aggregated EVs in a parking lot to flatten the energy demand curve, and reduce the peak load energy costs. In this process, a controller reads the real-time household power consumption data through smart meter, PV power generation under real environment, the state of-charge (SOC) for both EVs and battery storage, and the EV availability, to intelligently control the power flow from/to the energy sources to reduce the grid load demand. Additionally, an advanced charge management technique for both aggregated EVs and single EV is developed. On the contrary, the load management technique models a load-scheduling technique for a demand response (DR)-based home energy management systems (HEMS) that minimizes the electricity cost for the consumer and incorporates operational constraints for individual loads and energy sources. A Mixed-Integer Linear Programming (MILP)-based optimization model is formulated to determine the optimal scheduling of operation for residential loads and DERs according to a day-ahead time-of-use (TOU) electricity tariff. Peak load constraint is also incorporated into the optimization model to address grid reliability issues such as demand peaks, rebound peaks and congestion in the grid. The fmdings in this chapter suggest that an intelligent management technique can substantially reduce the power demand of the grid using EVs and reduce the impact of intermittent sources and thus improve the load factor.

Chapter Contents:

• List of abbreviations
• 5.1 Introduction
• 5.2 Power-demand management for single customer: Energy-resource-management technique
• 5.2.1 Load-management algorithm
• 5.2.2 EV charge management
• 5.2.3 Case studies
• 5.2.3.1 Case study 1
• 5.2.3.2 Case study 2
• 5.2.4 Comparison with an artificial neural network technique
• 5.3 Power-demand management for single customer: Load-management technique
• 5.3.1 HEMS model
• 5.3.2 Scheduling model
• 5.3.2.1 Optimization variables
• 5.3.2.2 Objective function
• 5.3.2.3 Constraints
• 5.3.3 Case study
• 5.3.3.1 Description of the case study
• 5.3.3.2 Simulation setup
• 5.3.3.3 Results and discussion
• 5.4 Power-demand management for multiple customers
• 5.4.1 General overview
• 5.4.2 Aggregated EV and power-demand management algorithm
• 5.4.3 Case studies
• 5.5 Conclusion
• References

Inspec keywords: power supply quality; load flow control; tariffs; demand side management; intelligent control; power meters; power generation control; costing; integer programming; battery storage plants; power generation scheduling; power generation reliability; photovoltaic power systems; power generation economics; vehicle-to-grid

Other keywords: grid reliability; electric vehicles; load-management approaches; household power consumption; battery storages; optimal scheduling; energy demand curve; optimization model; optimization techniques; grid load demand; operational constraints; control technique; photovoltaics system; load-scheduling technique; power flow; customer coordinates; smart grid; demand response-based home energy management system; powe-quality; charge management technique; PV power generation; smart meter; Power-demand management; intelligent management technique; electricity cost; day-ahead time-of-use electricity tariff; DR-based-HEMS; EV management technique; mobile energy sources; intelligent bidirectional energy transfer process; renewable-energy-based power systems; mixed-integer linear programming model; intelligently control; energy resource management technique

Subjects: Transportation; Power system management, operation and economics; Power system measurement and metering; Power supply quality and harmonics; Power system control; Other power stations and plants; Optimisation techniques; Control engineering computing; Reliability; Optimisation techniques; Control of electric power systems