Allocation of dump load in islanded microgrid using the mixed-integer distributed ant colony optimization with robust backward\forward sweep load flow

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonReliable planning and operation of droop-controlled islanded microgrids (DCIMGs) is fundamental to expand microgrids (MGs) scalability and maximize renewable energy potential. Employing dump loads (DLs) is a promising solution to absorb excess generation during off-peak hours while keeping voltage and frequency within acceptable limits to meet international standards. Considering wind power and demand forecast uncertainties in DCIMG during off-peak hours, the allocation of DL problem was modelled as two problems, viz., deterministic and stochastic. The former problem was tackled using four highly probable deterministic generation and demand mismatch scenarios, while the latter problem was formulated within scenario based stochastic framework for uncertainty modelling. The mixed-integer distributed ant colony optimization (MIDACO) was introduced as a novel application in microgrids to find the optimal location and size of DL as well as the optimal droop setting for distributed generation (DG). Furthermore, to enhance the convergence of the proposed optimization technique, three robust and derivative free load flow methods were developed as novel extensions of the original backward\forward sweep (BFS) for grid-connected MGs. The three load flow methods are called special BFS, improved special BFS, and general BFS. The first two methods rely on one global voltage variable distributed among all DGs, while the latter has more general approach by adopting local voltage at each generating bus. The deterministic multi-objective optimization problem was formulated to minimize voltage and frequency deviation as well as power losses. Inversely, the stochastic multi-objective problem with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The proposed method was applied to the IEEE 33-, 69-, and 118-test systems as modelled in MATLAB environment and further validated against competitive swarm and evolutionary metaheuristics. Various convergence tests were considered to demonstrate the efficacy of the proposed load flow methods with MIDACO’s non-dominated solution. Likewise, different optimization parameters were utilized to investigate their impact on the solution. Moreover, the advantage of multi-objective optimization against single objective was provided for the deterministic optimization problem, while the effect of load model and droop response were also investigated. The obtained results in chapter 5 and 6 further demonstrate the fundamental role of DL in voltage and frequency regulation while minimizing costs and energy losses associated with DCIMG operation. Accordingly, an improved voltage and frequency profiles for the system after DL inclusion were attained in Figure 6.9 and Figure 6.10, respectively. To demonstrate the competitiveness of DL-based energy management system (EMS) against storage-based EMS, a brief cost benefit analysis considering hot water demand was also provided

Heuristics for the distributed blocking Ffow shop scheduling problem

Postprint (published version

Efficient heuristics for the parallel blocking flow shop scheduling problem

We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them –especially the IGA– perform considerably better than those algorithms adapted from the DPFSP literature.Peer ReviewedPostprint (author's final draft

Distributed allocation of mobile sensing swarms in gyre flows

We address the synthesis of distributed control policies to enable a swarm of homogeneous mobile sensors to maintain a desired spatial distribution in a geophysical flow environment, or workspace. In this article, we assume the mobile sensors (or robots) have a "map" of the environment denoting the locations of the Lagrangian coherent structures or LCS boundaries. Based on this information, we design agent-level hybrid control policies that leverage the surrounding fluid dynamics and inherent environmental noise to enable the team to maintain a desired distribution in the workspace. We establish the stability properties of the ensemble dynamics of the distributed control policies. Since realistic quasi-geostrophic ocean models predict double-gyre flow solutions, we use a wind-driven multi-gyre flow model to verify the feasibility of the proposed distributed control strategy and compare the proposed control strategy with a baseline deterministic allocation strategy. Lastly, we validate the control strategy using actual flow data obtained by our coherent structure experimental testbed.Comment: 10 pages, 14 Figures, added reference

Optimization flow control -- I: Basic algorithm and convergence

We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using a gradient projection algorithm. In this system, sources select transmission rates that maximize their own benefits, utility minus bandwidth cost, and network links adjust bandwidth prices to coordinate the sources' decisions. We allow feedback delays to be different, substantial, and time varying, and links and sources to update at different times and with different frequencies. We provide asynchronous distributed algorithms and prove their convergence in a static environment. We present measurements obtained from a preliminary prototype to illustrate the convergence of the algorithm in a slowly time-varying environment. We discuss its fairness property

Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid

Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. During the formulation of the proposed algorithm, multi-phase power distribution system is considered which has unbalanced loadings, voltage control and reactive power compensation devices. The proposed algorithm is built upon a co-simulation framework that optimizes the objective by adapting a constriction factor Particle Swarm optimization. The proposed multi-phase OPF technique is validated using IEEE 8500-node benchmark distribution system.Comment: IEEE PES GM 2014, Washington DC, US

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System