Storage Size Determination for Grid-Connected Photovoltaic Systems

In this paper, we study the problem of determining the size of battery storage used in grid-connected photovoltaic (PV) systems. In our setting, electricity is generated from PV and is used to supply the demand from loads. Excess electricity generated from the PV can be stored in a battery to be used later on, and electricity must be purchased from the electric grid if the PV generation and battery discharging cannot meet the demand. Due to the time-of-use electricity pricing, electricity can also be purchased from the grid when the price is low, and be sold back to the grid when the price is high. The objective is to minimize the cost associated with purchasing from (or selling back to) the electric grid and the battery capacity loss while at the same time satisfying the load and reducing the peak electricity purchase from the grid. Essentially, the objective function depends on the chosen battery size. We want to find a unique critical value (denoted as $C_{ref}^c$) of the battery size such that the total cost remains the same if the battery size is larger than or equal to $C_{ref}^c$, and the cost is strictly larger if the battery size is smaller than $C_{ref}^c$. We obtain a criterion for evaluating the economic value of batteries compared to purchasing electricity from the grid, propose lower and upper bounds on $C_{ref}^c$, and introduce an efficient algorithm for calculating its value; these results are validated via simulations.Comment: Submitted to IEEE Transactions on Sustainable Energy, June 2011; Jan 2012 (revision

Quasi-dynamic Load and Battery Sizing and Scheduling for Stand-Alone Solar System Using Mixed-integer Linear Programming

Considering the intermittency of renewable energy systems, a sizing and scheduling model is proposed for a finite number of static electric loads. The model objective is to maximize solar energy utilization with and without storage. For the application of optimal load size selection, the energy production of a solar photovoltaic is assumed to be consumed by a finite number of discrete loads in an off-grid system using mixed-integer linear programming. Additional constraints are battery charge and discharge limitations and minimum uptime and downtime for each unit. For a certain solar power profile the model outputs optimal unit size as well as the optimal scheduling for both units and battery charge and discharge (if applicable). The impact of different solar power profiles and minimum up and down time constraints on the optimal unit and battery sizes are studied. The battery size required to achieve full solar energy utilization decreases with the number of units and with increased flexibility of the units (shorter on and off-time). A novel formulation is introduced to model quasi-dynamic units that gradually start and stop and the quasi-dynamic units increase solar energy utilization. The model can also be applied to search for the optimal number of units for a given cost function.Comment: 6 pages, 3 figures, accepted at The IEEE Conference on Control Applications (CCA

Regression Monte Carlo for Microgrid Management

We study an islanded microgrid system designed to supply a small village with the power produced by photovoltaic panels, wind turbines and a diesel generator. A battery storage system device is used to shift power from times of high renewable production to times of high demand. We introduce a methodology to solve microgrid management problem using different variants of Regression Monte Carlo algorithms and use numerical simulations to infer results about the optimal design of the grid.Comment: CEMRACS 2017 Summer project - proceedings

Corrective receding horizon EV charge scheduling using short-term solar forecasting

Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution

Multi-port Memory Design for Advanced Computer Architectures

In this thesis, we describe and evaluate novel memory designs for multi-port on-chip and off-chip use in advanced computer architectures. We focus on combining multi-porting and evaluating the performance over a range of design parameters. Multi-porting is essential for caches and shared-data systems, especially multi-core System-on-chips (SOC). It can significantly increase the memory access throughput. We evaluate FinFET voltage-mode multi-port SRAM cells using different metrics including leakage current, static noise margin and read/write performance. Simulation results show that single-ended multi-port FinFET SRAMs with isolated read ports offer improved read stability and flexibility over classical double-ended structures at the expense of write performance. By increasing the size of the access transistors, we show that the single-ended multi-port structures can achieve equivalent write performance to the classical double-ended multi-port structure for 9% area overhead. Moreover, compared with CMOS SRAM, FinFET SRAM has better stability and standby power. We also describe new methods for the design of FinFET current-mode multi-port SRAM cells. Current-mode SRAMs avoid the full-swing of the bitline, reducing dynamic power and access time. However, that comes at the cost of voltage drop, which compromises stability. The design proposed in this thesis utilizes the feature of Independent Gate (IG) mode FinFET, which can leverage threshold voltage by controlling the back gate voltage, to merge two transistors into one through high-Vt and low-Vt transistors. This design not only reduces the voltage drop, but it also reduces the area in multi-port current-mode SRAM design. For off-chip memory, we propose a novel two-port 1-read, 1-write (1R1W) phasechange memory (PCM) cell, which significantly reduces the probability of blocking at the bank levels. Different from the traditional PCM cell, the access transistors are at the top and connected to the bitline. We use Verilog-A to model the behavior of Ge2Sb2Te5 (GST: the storage component). We evaluate the performance of the two-port cell by transistor sizing and voltage pumping. Simulation results show that pMOS transistor is more practical than nMOS transistor as the access device when both area and power are considered. The estimated area overhead is 1.7�, compared to single-port PCM cell. In brief, the contribution we make in this thesis is that we propose and evaluate three different kinds of multi-port memories that are favorable for advanced computer architectures

Review of Researches on Techno-Economic Analysis and Environmental Impact of Hybrid Energy Systems

Hybrid energy systems, which are combinations of two or more renewable and non-renewable energy sources, have been identified as a viable mechanism to address the limitations of a single renewable energy source, utilized for electricity generation. In view of this, several research works have been carried out to determine the optimal mix of different renewable and non-renewable energy resources used for electricity generation. This paper presents a comprehensive review of the optimization approaches proposed and adopted by various authors in the literature for optimal sizing of hybrid energy systems. It is observed that the objective functions - considered by a large percentage of researchers to optimize the sizing of hybrid energy systems - are cost minimization of the generated electricity, system reliability enhancement and environmental pollution reduction. Other factors covered in the literature are equally discussed in this article. Similarly, simulation and optimization software used for the same purpose are covered in the paper. In essence, the main aim of this paper is to provide a scope into the works that have been carried out in the field of hybrid energy systems, used for electricity generation with the view to informing researchers and members of the public alike, on trends in methods applied in optimal sizing of hybrid energy systems. It is believed that the information provided in this paper is very crucial in advancing research in the field

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000