• Energy Research

Demand response has been one of the efficient and highly effective energy management solutions for the smart grid environment, which uses various load curtailment and scheduling policies to minimize energy consumption and peak load demand. Recent advances in direct load control techniques show a significant reduction in peak load demand, resulting in smoother load profiles. However, a majority of the existing work misses out on the practical aspects where appliances are of mixed categories, viz., preemptive, non-preemptive, deferrable, non-deferrable, etc. In this paper, we consider addressing the problem of scheduling deferrable and non-deferrable appliances that have inter-dependency constraints among them. Considering the complexity of the problem, we propose a greedy algorithm to obtain near-optimal solutions relatively quicker than the optimal solutions. The performance of the proposed algorithm has been evaluated and compared with existing algorithms on real-world power consumption data. The results obtained show that the proposed mechanism is highly efficient and produces schedules with a (Formula presented.) lesser peak as compared to the existing algorithms. Demand response has been one of the efficient and highly effective energy management solutions for the smart grid environment, which uses various load curtailment and scheduling policies to minimize energy consumption and peak load demand. Recent advances in direct load control techniques show a significant reduction in peak load demand, resulting in smoother load profiles. However, a majority of the existing work misses out on the practical aspects where appliances are of mixed categories, viz., preemptive, non-preemptive, deferrable, non-deferrable, etc. In this paper, we consider addressing the problem of scheduling deferrable and non-deferrable appliances that have inter-dependency constraints among them. Considering the complexity of the problem, we propose a greedy algorithm to obtain near-optimal solutions relatively quicker than the optimal solutions. The performance of the proposed algorithm has been evaluated and compared with existing algorithms on real-world power consumption data. The results obtained show that the proposed mechanism is highly efficient and produces schedules with a 14% lesser peak as compared to the existing algorithms.

Residential, commercial, and industrial buildings have been reported to consume a large portion of the generated energy. With the introduction of smart grid and its energy optimization techniques, it is now possible to efficiently manage and control consumers’ energy usage to fulfil their demands with the existing energy generation infrastructure, which otherwise seems to be a backbreaking challenge. This paper presents an efficient energy management solution for buildings with a large number of thermostatic devices (air conditioners) that maintain the temperature of different thermal zones in a predefined range. The primary objective of this paper is to schedule the thermostatic devices in order to reduce total energy consumption by these devices when they are in operation for a very long duration of time, while maintaining the other constraints. We formulate it as a graph problem where minimum mean cycle will provide the desired solution. The proposed methodology ensures that at no point in time the power consumption goes beyond a certain peak power consumption limit. We also enhance the methodology to reduce peak load consumption. Furthermore, a fast greedy approach has been developed to efficiently scale up the aforementioned scheduling scheme for a large number of devices. Experimental results show that significant improvements can be obtained by the proposed approaches over existing algorithms in reducing average energy consumption.

In this paper, we propose efficient load scheduling based demand side management schemes for the objective of peak load reduction. We propose two heuristic algorithms, named G-MinPeak and LevelMatch, which are based on the generalized two-dimensional strip packing problem, where each of the appliances has their specific timing requirements to be fulfilled. Furthermore, we have proposed some improvement schemes that try to modify the resulted schedule from the proposed heuristic algorithms to reduce the peak. All the proposed algorithms and improvement schemes are experimented using benchmark data sets for performance evaluation. Extensive simulation studies have been conducted using practical data to evaluate the performance of the algorithms in real life. The results obtained show that all the proposed methodologies are thoroughly effective in reducing peak load, resulting in smoother load profiles. Specifically, for the benchmark datasets, the deviation from the optimal values has been about 6% and 7% for LevelMatch and G-MinPeak algorithms respectively and by using the improvement schemes the deviations are further reduced up to 3% in many cases. For the practical datasets, the proposed improvement schemes reduce the peak by 5.21− 7.35 % on top of the peaks obtained by the two proposed heuristic algorithms without much computation overhead.

Direct load control has been an efficient and effective load control mechanism under demand side management in smart grid. With this technique, consumers' energy consuming appliances are managed and controlled by central utility with the primary objective of load profiling and to reduce peak load demand. In this paper, we take up the problem of scheduling under peak load constraint for controllable appliances that allows them to be run on various energy consumption levels. The problem is a unique manifold optimization problem where before scheduling a device, we are required to select a specific power consumption level for it that would result in minimizing overall finishing time (delay minimization). We model this problem into two-dimensional bin packing problem and present mathematical programming formulation, while proving it to be NP-hard. We have obtained optimum solutions for 20 numbers of appliances using IBM ILOG CPLEX optimization solver. Further a number of approximation algorithms have been used to obtain solutions quickly.

The worldwide energy consumption has been growing in aggregate at a tremendous rate, and a majority of the same is due to heating ventilation air conditioning (HVAC) loads in urban buildings. With the help of the recent advances in energy management and optimization techniques, the operations and functioning of these devices can now be managed and controlled efficiently for an improved energy consumption scenario and thereby reducing cost. In this article, we propose a multiobjective optimal scheduling framework based on Johnson's elementary circuit finding algorithm for controlling HVAC devices, specifically for buildings that require continuous thermal comfort maintenance. Two primary objectives addressed in this article are: minimizing power fluctuation and maximizing thermal comfortability of the users. We use standard comfortability indices to quantify thermal comfortability. To reduce the computation time, we also propose two intelligent improvement schemes that prune the exponential search space of Johnson's algorithm. Furthermore, a new greedy scheduling algorithm has been proposed to obtain near-optimal solutions efficiently. All the proposed approaches have been studied in a simulated environment depicting a real-world scenario to evaluate their efficiency and effectiveness for practical implementations, including a comparative analysis with Karp's minimum mean cycle algorithm in this problem setup.