Muhammad Babar

The consumer rationality assumption in incentive based demand response program via reduction bidding

Because of the burgeoning demand of the energy, the countries are finding sustainable solutions for these emerging challenges. Demand Side Management is playing a significant role in managing the demand with an aim to support the electrical grid during the peak hours. However, advancement in controls and communication technologies, the aggregators are appearing as a third party entity in implementing demand response program. In this paper, a detailed mathematical framework is discussed in which the aggregator acts as an energy service provider between the utility and the consumers, and facilitate the consumers to actively participate in demand side management by introducing the new concept of demand reduction bidding (DRB) under constrained direct load control. Paper also presented an algorithm for the proposed framework and demonstrated the efficacy of the algorithm by considering few case studies and concluded with simulation results and discussions.

The development of demand elasticity model for demand response in the retail market environment

In the context of liberalized energy market, increase in distributed generation, storage and demand response has expanded the price elasticity of demand, thus causing the addition of uncertainty to the supply-demand chain of power system. In order to cope with the challenges of demand uncertainty under the unbundled electricity market, the concept of Market-based Control Mechanism (MCM) in retail market environment has been emerging. This paper presents the concept considering demand elasticity as an opportunity in retail market environment for inventing a new bid mechanism. This work formulates demand elasticity model as a Markov decision problem and implements pursuit algorithm as a machine learning technique to evaluate the price elasticity of demand by predicting the price. The performance of the algorithm is compared with the numerical calculation of price elasticity of demand for the given simulation settings.

A Novel and Smart Design of Superconducting Fault Current Controller: Implementation and Verification for Various Fault Condition

By the advent of the Smart Grid and integration of distributed generators, electrical networks are facing uncountable challenges. The existing protection schemes that simply limit the fault current to the predetermined set values may not perform optimally, and even the existing protection coordination schemes fail and lead to catastrophic failures in the increasingly complex and unpredictable grid operation. This paper proposes a novel and smart design of fault current controller constituting a full-bridge thyristor rectifier embedding a superconducting coil. When a fault occurs and the resulting current through the superconducting coil exceeds a certain preset value based on the current operating conditions of the grid to maintain the grid integrity, the magnitude of the fault current is regulated to a desired value by automatic controlling of the thyristor. This research also implements a lab-scale Smart FCC with smart current control capability and demonstrates the desired functionality and efficacy of design by changing the fault conditions. This proposed Smart FCC design will make the Smart Power Grid capable of self-healing against current faults.

Novel algorithm for aggregated demand response strategy for smart distribution network

Advancement in demand side management strategies enables smart grid to cope with the ever increasing energy demand and provide economic benefit to all of its stakeholders. Moreover, emerging concept of smart pricing and advances in load control can provide new business opportunities for demand side management service provider or aggregator. The aggregator act as a third party between the electricity supply system and the consumers, and facilitate consumers to actively participate in Demand Side Management (DSM) by bidding price against power reduction with some constraints. This work develops a novel algorithm for aggregated demand response for smart distribution network. Simulations are carried out which identify the demand reduction bids and consumer constraints. The simulation results of the proposed algorithm demonstrate the potential impact of an aggregated demand response on the power system.

Implementation of a demand elasticity model in the building energy management system

Nowadays, crucial part of modern Building Automation and Control Systems (BACS) is electric energy management. An active demand side management is very important feature of a Building Energy Management Systems (BEMS) integrated within the BACS. Since demand value changes in time and depends on various events, factors and parameters, a demand elasticity model has been proposed to provide reliable information about current and expected energy demand. In this paper we propose extension of this model with respect to parameters available in the BACS, determining energy demand level. Real data from the BACS had been imported into a calculation algorithm and proposed approach has been verified in simulation. For easy implementation of the demand elasticity model in the BACS, an extension for logical interface with a new functional profile has been proposed and described. It is ready for integration within the BACS with Internet of Things paradigm.

Consolidated demand bid model and strategy in constrained Direct Load Control program

With the development of the Smart Grid, Direct Load Control (DLC) can be implemented in such a way that consumer can be motivated to participate in it while satisfying ON/OFF constraints of his/her devices. This paper introduces the concept of Consolidated Demand Reduction Bid (CDRB) and develops a dynamic algorithm to compute the same for a consumer having a set of devices with different ratings, importance and constraints. CDRB consist of various power levels at which consumer is willing to curtail its load during a particular control interval Pl(k) and the corresponding bid to curtail Pl(k) units of power for a specified duration is F(CPl(k)). The proposed algorithm can be implemented using two way communication between the consumer and the service provider. The applicability of the dynamic algorithm is illustrated using a cases study. The dynamic nature of the algorithm is also illustrated for different choices of the service provider.

Demand response: Social welfare maximisation in an unbundled energy market - case study for the low-voltage networks of a distribution network operator in the Netherlands

With the introduction of smart meters, dynamic pricing and home energy management systems, residential customers are able to react to changes in electricity prices. In an unbundled market, the energy supplier and the network operator may have conflicting interests with respect to demand response (DR) programs. As the customer participation is essential to a well-functioning DR program, it is needed to assess which DR programs offers the most benefits to customers. Two DR program options are analysed for low-voltage feeders: a program from the energy supplier based on the electricity price, and a demand response program from the network operator based on the loading of the network. Depending on the grid topology the benefits can change significantly between the two DR programs. DR from an energy supplier point of view might induce under-voltages which lead to grid reinforcements, while load shifting from a network point of view can generate higher electricity cost.

Performance comparison of different filter applications in three-phase PFC rectifier

In this paper, a 3-phase AC/DC converter (six switches) with power factor correction (PFC) is presented. In order to reduce the harmonics due to the high switching frequency of the converter (20kHz); a L-filter, a LCL passive filter and three LCL active filters are designed for comparison with eachother. The paper concentrates on the methods to find the parameters of these filters and discusses their control mechanism. Comparison of these different filters is carried out by simulation results and discusses their pros and cons over one another.

Demand dispatch for different demand response schemes: A consolidated solution

Advance infrastructures have changed the passive consumers into active because now they can share information, perform automatic control as well as directly influence the electricity market via demand response (DR) programs. Till today, many DR Programs are proposed in Smart Grid (SG) paradigm and are facing enormous challenges. This paper concerns the DR scheduling problem of the dispatchable loads at the end consumer premises. In DR Programs, the electricity prices vary over time and users receive the reward payment by the energy service providers, if the users modify the consumption during DR events. Thus, the paper devises the independent atomic dispatchable model which could be used for investigation of different kinds of DR Programs. The another objective of the paper is to formulate the generic scheduling problem i.e. how to schedule the operation of the dispatchable loads, taking in account the overall energy cost, the comfort level and the timeliness. Paper also performs the simulations for two different DR Programs namely Time-Of-Use (TOU) and Energy Bidding (EB), and results show that the scheduling problem is suitably formulated for real-life instances and is applicable to other types of loads

Learning technique for real-time congestion management in an active distribution networks

During the last few decades, the concept of demand response (DR) in the energy sector has gained substantial momentum. Research has led to a range of DR solutions. These solutions mostly differ in their applications, the hosting power system, the energy market etc. Moreover, as per the EU directive, DR aggregators should be allowed to trade DR alongside supply in both day-ahead and real time electricity markets. Meanwhile, independent aggregators do not consider physical limitations of a network, thus setting up new a challenges for network operation. In this paper, an active learning technique for real-time congestion management is proposed to tackle this challenge. This enables distributed system operator (DSO) to incenticize independent aggregators efficiently in order to use DR for overloading mitigation. Lastly, a case study is simulated which verifies the performance of a new approach for congestion management.