T. P. Imthias Ahamed

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.

Pursuit Algorithm for optimized load scheduling

Demand Response (DR) has gained importance in the recent years. The prime objective of DR is to reduce the maximum demand. There are various DR schemes. This study focuses on price based DR schemes in which consumer is motivated to change his load demand pattern by pricing based incentives. The paper presents an algorithm to solve the load scheduling problem under variable pricing scheme. To this end the paper formulates the load scheduling problem as a singe stage decision making problem and solves it using Pursuit Algorithm, which maintains an agreeable balance between exploration and exploitation. An algorithm to generate realistic loads is also presented. Simulation experiments were conducted considering different types of loads and pricing.

Demand response in Saudi Arabia

Demand Response is a useful tool to limit the maximum demand thereby reducing the need of peak power generation units which are normally less efficient and operational for only small time interval. DR enables consumers to manage their consumption according to the available generation which means electric supply is generation oriented rather than demand oriented. Considering the challenges facing Saudi Arabia in electrical sector, demand response initiative can be very beneficial. This paper describes various demand response methods in general followed by the simulation of variable price structure and incentive based methods for possible DR implementation.

A simulated annealing algorithm for demand response

For many consumers there are loads which need to be on for a subinterval between two time instants, but it is immaterial during which subinterval it is run. In many price based Demand Response (DR) programs such as Time of Use (TOU), Critical Peak Pricing (CPP), Extreme Day Pricing customers are informed about the prices on a day ahead bases. By scheduling the subintervals during which the loads are used consumers can minimize their electricity bill and by so doing the maximum demand on the system will decrease. This paper formulates this as an optimization problem and a simulated annealing based algorithm is used to find the optimum schedule. The algorithm is scalable and is applicable to a broad category of loads.

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.

Reinforcement learning in power system scheduling and control: A unified perspective

Reinforcement Learning (RL) has been applied to various scheduling and control problems in power systems in the last decade. However, the area is still in its infancy. In this paper, we present various research works in this area in a unified perspective. In most of the applications, power system problems — control of FACTS devices, reactive power control, Automatic Generation Control, Economic Dispatch, etc — are modeled as a Multistage Decision making Problem and RL is used to solve the MDP. One important point about RL is, it takes considerable amount of time to learn a control strategy. However, RL can learn off line using a simulation model. Once the control strategy is learned decision making can be done almost instantaneously. A major drawback of RL is most of the application does not scale up and much work need to be done. We hope this paper will generate more interest in the area and RL will be utilized to its full potential.

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.

Analysis of adaptability of Reinforcement Learning approach

Reinforcement Learning is a powerful tool which is being used for solving many optimization problems including power system scheduling problems. Even though there are theoretical results which suggest that under specified technical conditions, RL algorithms are adaptive, however, for power system scheduling problems the potential of adaptability is not still explored. In this paper, we explore, through simulation studies, the adaptability of an RL algorithm considering a simple multi stage decision making problem.

Novel diverse tariff scheme to enhance demand response

With the development of smart grid technology, price based demand response (PBDR) scheme will gain momentum and utilities are expected to enforce time dependent tariff. In near future, more and more domestic consumers will use different algorithms to schedule their flexible demand in order to reduce their energy costs. It is well known that with a single time of use tariff most of the customers schedule their load in the lower price intervals, hence their electricity cost will reduce but maximum demand (MD) on the power grid will not decrease. This paper proposes novel tariff scheme in which domestic consumers are split into “n” load groups and each group will have a separate tariff scheme. It is shown through simulations that with such a tariff scheme residential customers will be able to cut down their electricity bill while at the same time the utilities can bring down the maximum demand (MD).

Online adaptive algorithm for scheduling PEV charging

Plug-in electric vehicles (PEVs) have been increasing in recent years and are expected to keep growing even more. PEVs are charged by using the electricity from the power grid, which leads to higher electricity demands. If PEVs are charged randomly without proper management, the charging might occur during peak periods. Therefore, the charging could cause severe problems to the power system. One way to mitigate this problem is for utilities to employ time dependent pricing such as Critical Peak Pricing or Real Time Pricing (RTP). This paper presents a Learning Automata (LA) algorithm for scheduling PEV under RTP. To train the algorithm and for a good initial schedule, it is used offline with the previous available data, then the algorithm is used online for the current data. It is shown that for a large fleet of PEVs, it might take a few days to learn the best schedule. However, even during this period, the results are better than unscheduled charging. The paper shows that with a proper scheduling algorithm, we can minimize the charging cost of a large number of PEVs.