Browsing by Author "Kiran, P. B.S."

Distributed energy management algorithms are remarkably efficient in resolving load variation issues and addressing a high peak to average power ratio. A distributed algorithm promotes individual participation, bilateral coordination, data privacy, and consumers' economic benefit. This paper presents an alternating direction method of multipliers (ADMM) based distributed algorithm for energy management in a distribution network. It decomposes the centralized energy management problem into a distribution system operator (DSO) and load aggregator (LA) level sub-problems. The DSO objective is to minimize energy cost and power loss in the network, whereas LA intends to reduce its energy consumption cost. The DSO and LA optimization problems are solved independently and coordinated via a central coordinator. The algorithm is tested on a modified 15-node distribution network to substantiate its effectiveness. The simulation results are presented for flexible loads, solar PV generation, and battery energy storage systems. Also, we analyzed the impact of the penalty factor on the algorithm convergence and the network power consumption.

This paper develops a centralized demand response (DR) framework to schedule the end user loads by the authority of aggregator. The aim of the aggregator is to schedule the customers' load demand and is formulated as a stochastic cost minimization optimization including phase unbalance and the peak-to-average ratio (PAR) as constraints. The optimization problem considerers the uncertainty in electricity price and solar PV generation. All the optimization formulations are modeled as a mixed integer linear program (MILP). The proposed framework is studied on secondary distribution feeder where 60 residential customers are connected in the system.

Microgrids are growing at a right pace due to their advantages towards the economical environmental sustainability. Effective operation and management of microgrids can significantly help both the microgrid operator and customers to get economic and technical benefits. In addition to distributed generation (DG) and battery energy storage system (BESS), a microgrid can effectively utilize demand response (DR) strategy to better manage the energy and improve the performance of the network. In this paper, a mathematical formulation for day-ahead energy management of a microgrid is developed. The day-ahead scheduling of resources have been done with the available information of DG, load demand and electricity price. The simulation case studies with two DR schemes have been carried out for four bus test system and results are presented to compare the DR schemes.

The proliferation of microgrids is rapidly growing into an existing grid due to its advantages like reducing the stress on the main grid and increase the competition in the electricity market. In such a competitive scenario, gaining maximum benefit is essential for both the microgrid aggregator and consumer. To address that paper presents a mathematical framework to schedule the local generating resources like a diesel generator, battery energy storage system (BESS) and price setting to a consumer. The objectives of the aggregator and consumers are complementary to each other. To satisfy both the players an equilibrium problem has formulated. In general, it can be solved by using bi-level programming; this work presents a single-stage framework to meet both the aggregator and consumer requirements without violating the network constraints. And the effect of demand response and uncertainty of random variables like day-ahead electricity price, solar PV, wind power, load demand on the offer price is also studied. The developed framework formulated as a mixed integer non-linear program and test over a 33-bus active radial distribution network.

The expansion of smart grids and increased participation of energy management programs have to deal with the large amount of data transferring, processing, and storage. Large amount of data increases the cost of communication and possibly data loss. Updating the communication protocols to accommodate the increased memory size and computational processing incurs additional expenditure for implementation. This paper developed a Quantized Gradient Multiplier (QGM) based energy management algorithm with limited data communication. This method is studied on IEEE 13-bus and 15-bus network. The simulation results are compared with the Lagrangian Multiplier method and show that the QGM based energy management performs better with the reduced energy data set. Thereby, the suggested method benefits to the load aggregators in a real-time energy market operation with limited energy data communication.

Demand response (DR) is an effective tool for energy management at the consumer end. The effectiveness of DR model depends not only on cost minimization but also on consumer satisfaction and comfort level. The satisfaction of the consumer is measured by benefit functions. In this paper, three benefit functions viz. Quadratic, logarithmic, and exponential have been modeled to study the DR algorithm for effective energy management. And also suggested a method to calculate approximated benefit function. The simulation results are presented for different scenarios.