K Shanti Swarup

Non-consistency of energy availability from Renewable Energy Sources needs estimation and scheduling in advance so that the other certain sources of energy like fuel cells, diesel generators, storage devices etc., can be scheduled appropriately to maintain load-generation balance in real time. Evolutionary program techniques are proving handy and reliable in the process. This article uses an Artificial Fish Swarm algorithm to solve the problem of day-ahead scheduling of generation in a mix of Renewable Energy Sources, despatchable sources and storage. The utility function of hourly generation cost is considered for optimization along with various microgrid operational constraints. The performance of the algorithm is validated by applying to schedule generation in a microgrid in grid connected mode consisting of one wind turbine and one PV source as Renewable energy sources, one diesel generator and fuel cell as despatchable generators and a battery for storage. The scheduled generation of each generator, power exchange of storage source along with its state of charge are evaluated for optimum cost of generation.

The work proposed in this article proposes a different energy management scheme for residential consumers having higher penetration of solar power and heating loads. The presented methodology assumes the heating loads present in the residence as non-critical loads and tries to operate them in power saving mode, whenever necessary. In the considered scheme non-critical loads are connected in series with AC electric springs which in turn schedule them as per the requirement. The major scope of this work is to make the non-critical loads consume lesser power by pressing springs into action in the presence of solar power. It is expected that the springs compliment the efforts of solar panels in reduction of power consumed from utility grid, daily peaks and energy purchase from the utility grid. In order to implement the proposed methodology a completely customer driven home energy management algorithm is proposed and the robustness of the same is tested on a residential consumer, considering a 24 load curve. The simulation studies for the projected methodology are performed in MATLAB.

This paper presents a new multi-objective harmony search (MOHS) algorithm for environmental/economic dispatch (EED) problem. The EED problem is formulated as a non linear and constrained optimization problem with competing and non-commensurable objectives. The two competing objectives, fuel cost and emission, were optimized simultaneously using the proposed MOHS algorithm. The MOHS algorithm uses a non dominated sorting and ranking procedure with dynamic crowding distance to develop and maintain a well distributed Pareto-optimal set. The proposed algorithm has been tested on the standard IEEE 30 bus and 118 bus systems. Simulation results are compared with the fast non dominated sorting genetic algorithm (NSGA-II) method. The results clearly show that the proposed method is able to produce a well distributed Pareto-optimal solutions than the NSGA-II method. © 2011 Elsevier B.V. All rights reserved.

This paper analyses the False Data Injection Attack (FDIA) on tie-line sensor measurements of a Load Frequency Control (LFC) System from an attacker's perspective. The attack vector has been modeled considering an advanced LFC system, wherein, the measurements undergo State Estimation and bad data detection algorithms. Two different attack vector modeling scenarios have been considered: in the first model, an arbitrary attack vector modeling is performed to study its effect on the system operation; The later model is built systematically to induce sequential outages. The significant advantage of the proposed method is that the number of measurements to be attacked and system knowledge like topology data, line parameter values, transfer function parameters that are required is very low. The results are studied for an IEEE 39 bus, 3 area system.

In an isolated micro-grid electronically interfaced DG (EI-DG) units should also participate in frequency and voltage control along with synchronous generator based DG (SG-DG) units to ensure power quality and stability of the micro-grid. The conventional way of controlling frequency and voltage is to balance active power and reactive power of the system by implementing droop characteristics (P-f, Q-V) for EI-DG units with PI controllers and automatic generation control (AGC) and automatic voltage regulator (AVR) for SG-DG units. With the advent of smart micro-grids, a centralized control for both frequency and voltage using model predictive controller (MPC) is an alternative to the conventional controllers. The optimization capabilities of MPC make it suitable for frequency and voltage control of micro-grids equipped with fast and reliable communication. This paper investigates the performance of a centralized discrete model predictive controller (DMPC) in an isolated micro-grid with photovoltaic (PV) and diesel generators. The small signal dynamic models of the PV converter and SG-DG are considered in the design of the DMPC. The micro-grid network and loads are represented using steady state power balance equations. The performance of the DMPC is tested using MATLAB software package.

The Smart Grids are the future vision of the electric power system with integrated communication, protection, control and sensing technologies. With the introduction of new technologies which constitute the smart grid like demand response, demand side management, electric vehicles, energy storage systems, distributed energy resources, integration of renewable energy resources, and forecasting methods like artificial neural networks, deep learning methods etc, the scope of planning and operation of a smart grid has broadened. The new technologies bring in the need for better tools for solving the planning and operation problems. This paper aims to provide a survey of the works related to some of the smart grid components and classifies the works based on the computational intelligence tools used in solving the planning or operation problem.

This article proposes a system of stochastic mixed complementarity problems (MCP) for calculating the Bayesian Nash equilibrium (BNE) in an electricity spot market. The generators submit strategic multilevel price-offer functions under incomplete information about their rivals' marginal cost. This strategic multilevel price offering of each generator is modeled as a standard stochastic bilevel program which is then reformulated as its equivalent MCP. The merger concept is employed and formulated to model multilevel price-offer functions. We then propose the BNE-MCP model which solves the stochastic MCP models of all generators together. A scenario reduction technique is also developed to accurately model the wind and demand uncertainty in the proposed BNE-MCP model. The proposed BNE-MCP model is carefully studied on two illustrative examples. The modified IEEE 14-, 30-, and 57-node systems are also examined in our article.

Energy management engineers are focusing their interest in tapping maximum profit for their system from Substation automation (SSA)/Distribution automation (DA). Volt/Var control through Fixed/Switched capacitors, Transformer taps and Voltage Set points are at different levels of research and implementation. A Neural Network based solution for Voltage- VAR control is proposed with the aim to reduce the real power loss flowing in a power system and subsequently improve the voltage profile. The module consists of two networks. The first network determines the control parameters i.e., generator voltage, transformer taps and shunt capacitance for minimal power loss when the loads at the load buses are specified as inputs. With the obtained parameters, a load flow program is run and power loss is noted and the system is checked for voltage violations. In case of voltage violations, the voltages are fed to the second network, which gives dQ at different buses for voltage violation minimization. These modules are successfully tested for different load patterns on a six-bus system. © 2005 IEEE.

A novel convexified linear program for optimal directional overcurrent relay (DOCR) coordination problem (CP) is proposed in this letter. The DOCR CP is a highly constrained nonlinear nonconvex problem which was solved using various traditional and heuristic optimization techniques in the past. In this letter, the CP is formulated as a linear programming problem without fixing the current pickup settings of DOCRs. The bilinear terms in the proposed formulation are written in the form of linear inequalities using McCormick envelopes. A sequential tightening algorithm is used to update the boundary limits of each parameter based on the solution obtained in each iteration, thus tightening the convex hulls and leading to solutions near to global optimum. The performance of the proposed method is applied to various test systems among which the results of three bus test system are presented.