Multistage Expansion Planning of Active Distribution Systems: Towards Network Integration of Distributed Energy Resources

Over the last few years, driven by several technical and environmental factors, there has been a growing interest in the concept of active distribution networks (ADNs). Based on this new concept, traditional passive distribution networks will evolve into modern active ones by employing distributed energy resources (DERs) such as distributed generators (DGs), energy storage systems (ESSs), and demand responsive loads (DRLs). Such a transition from passive to active networks poses serious challenges to distribution system planners. On the one hand, the ability of DGs to directly inject active and reactive powers into the system nodes leads to bidirectional power flows through the distribution feeders. This issue, if not adequately addressed at the design stage, can adversely affect various operational aspects of ADNs, specifically the reactive power balance and voltage regulation. Therefore, the new context where DGs come into play necessitates the development of a planning methodology which incorporates an accurate network model reflecting realistic operational characteristics of the system. On the other hand, large-scale integration of renewable DGs results in the intermittent and highly volatile nodal power injections and the implementation of demand response programs further complicates the long-term predictability of the load growth. These factors introduce a tremendous amount of uncertainty to the planning process of ADNs. As a result, effective approaches must also be devised to properly model the major sources of uncertainty. Based on the above discussion, successful transition from traditional passive distribution networks to modern active ones requires a planning methodology that firstly includes an accurate network model, and secondly accounts for the major sources of uncertainty. However, incorporating these two features into the planning process of ADNs is a very complex task and requires sophisticated mathematical programming techniques that are not currently available in the literature. Therefore, this research project aim to develop a comprehensive planning methodology for ADNs, which is capable of dealing with different types of DERs (i.e., DGs, ESSs, and DRLs), while giving full consideration to the above-mentioned two key features. To achieve this objective, five major steps are defined for the project. Step 1 develops a deterministic mixed-integer linear programming (MILP) model for integrated expansion planning of distribution network and renewable/conventional DGs, which includes a highly accurate network model based on a linear format of AC power flow equations. This MILP model can be solved using standard off-the-shelf mathematical programming solvers that not only guarantee convergence to the global optimal solution, but also provide a measure of the distance to the global optimum during the solution process. Step 2 proposes a distributionally robust chance-constrained programming approach to characterize the inherent uncertainties of renewable DGs and loads. The key advantage of this approach is that it requires limited information about the uncertain parameters, rather than perfect knowledge of their probability distribution functions. Step 3 devises a fast Benders decomposition-based solution procedure that paves the way for effective incorporation of ESSs and DRLs into the developed planning methodology. To this end, two effective acceleration strategies are proposed to significantly enhance the computational performance of the classical Benders decomposition algorithm. Eventually, Steps 4 and 5 propose appropriate models for ESSs and DRLs and integrate them into the developed planning methodology. In this regard, a sequential-time power flow simulation method is also proposed to incorporate the short-term operation analysis of ADNs into their long-term planning studies. By completing the above-defined steps, the planning model developed in Step 1 will be gradually evolved, so that Step 5 will yield the final comprehensive planning methodology for ADNs

On feasibility, stability and performance in distributed model predictive control

In distributed model predictive control (DMPC), where a centralized optimization problem is solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm, i.e. the amount of communication between subsystems, as small as possible. At the same time, the number of iterations must be enough to give a feasible solution to the optimization problem and to guarantee stability of the closed loop system. In this paper, a stopping condition to the distributed optimization algorithm that guarantees these properties, is presented. The stopping condition is based on two theoretical contributions. First, since the optimization problem is solved using dual decomposition, standard techniques to prove stability in model predictive control (MPC), i.e. with a terminal cost and a terminal constraint set that involve all state variables, do not apply. For the case without a terminal cost or a terminal constraint set, we present a new method to quantify the control horizon needed to ensure stability and a prespecified performance. Second, the stopping condition is based on a novel adaptive constraint tightening approach. Using this adaptive constraint tightening approach, we guarantee that a primal feasible solution to the optimization problem is found and that closed loop stability and performance is obtained. Numerical examples show that the number of iterations needed to guarantee feasibility of the optimization problem, stability and a prespecified performance of the closed-loop system can be reduced significantly using the proposed stopping condition

Multi-Criteria Optimization of Real-Time DAGs on Heterogeneous Platforms under P-EDF

This paper tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each DAG having a minimum inter-arrival period for its activation requests, and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with DVFS capabilities, including big.LITTLE Arm architectures, and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by: minimizing power consumption, respecting precise timing constraints; maximizing the applications’ slack, respecting given power consumption constraints; or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs-hardware implementation choice for tasks that can be hardware-accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals

Performance Models for Data Transfers: A Case Study with Molecular Chemistry Kernels

With increasing complexity of hardwares, systems with different memory nodes are ubiquitous in High Performance Computing (HPC). It is paramount to develop strategies to overlap the data transfers between memory nodes with computations in order to exploit the full potential of these systems. In this article, we consider the problem of deciding the order of data transfers between two memory nodes for a set of independent tasks with the objective to minimize the makespan. We prove that with limited memory capacity, obtaining the optimal order of data transfers is a NP-complete problem. We propose several heuristics for this problem and provide details about their favorable situations. We present an analysis of our heuristics on traces, obtained by running 2 molecular chemistry kernels, namely, Hartree-Fock (HF) and Coupled Cluster Single Double (CCSD) on 10 nodes of an HPC system. Our results show that some of our heuristics achieve significant overlap for moderate memory capacities and are very close to the lower bound of makespan

The development of a carbon roadmap investment strategy for carbon intensive food retail industries

This work presents an approach to develop an innovative decarbonisation investment strategy framework for carbon intensive UK industries by using statistical analysis and optimisation modelling. The case study focuses on taking a representative sample of retail buildings and assesses the financial viability of installing low-carbon Combined Heat and Power units (CHPs) and Photovoltaic Solar Panels (PVs) across a portfolio of buildings. Simulation of each building are initially conducted, and the results generate a set of regression coefficients, via a multivariate adaptive regression splines (MARS), which are inputted into a Mixed Integer Linear Programming (MILP) problem. Solving the MILP yields the optimal decarbonisation investment strategy for the case study up to 2050, considering market trends such as electricity prices, gas prices and policy incentives. Results indicate the level of investment required per year, the operational and carbon savings associated, and a program for such investments. This method is reiterated for several scenarios where different parameters such as utility prices, capital costs and grid carbon factors are forecasted up to 2050 (following the Future Energy Scenarios from National Grid). This work shows how a clear mathematical framework can assist decision-makers in commercial organisations to reduce their carbon footprint cost-effectively and thus reach science-based targets

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Pey et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. Results: We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. Conclusions: A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.Basque Governmen