Optimal power control strategy of a distributed energy system incorporating demand response

Abstract: This paper presents an optimal power control scheduling of a distributed energy system in presence of demand response. The distributed energy system comprises of a solar photovoltaic (PV) module and a battery bank storage system. A non-convex mixed binary integer programming technique is used to model flexible and inflexible smart home appliances. Two scenarios are considered in the case study. The results show that efficient scheduling of smart home appliances combined with optimal control of distributed energy system can significantly reduce the total daily electricity cost by more than 50%. The optimal control of distributed energy system was also shown to have an effect on the scheduling of smart home appliances

Two-Stage Method for Optimal Operation of a Distributed Energy System

In this paper, a gas turbine-based distributed energy system (DES) model is developed for the design of operation planning. An operation mode aimed to optimize the operation of this DES is proposed. A multi-objective cost function considering the total system efficiency and operational cost is formulated for the optimal design of DES operation and control. A two-stage approach combining the particle swarm algorithm (PSO) with the sequential quadratic programming (SQP) method is employed to solve the nonlinear programming problem. Optimal operation strategies for the DES are investigated using the proposed two-stage method under three different demand loads in terms of weather conditions. The simulation results are compared with those using traditional rule-based operation methods. It is found that under the proposed operation mode, the DES is capable of achieving an improved performance in terms of thermal efficiency and operational cost

Paired Storage Distributed Energy System Design for a Local Community Farm

This project considers the design of a renewable energy microgrid for a 19-acre community farm in San Luis Obispo, CA as the farm seeks to increase the magnitude of its electrical loads, and gain back-up electrical capability. The microgrid design will enable lower carbon emissions, reduce demand on the utility grid while saving on energy costs, and provide improved reliability and resiliency to the operator of the community farm. The eventual implementation of the design by a professional engineering entity will allow the community farm to expand its education program to include renewable energy as well as gain notoriety in the sustainability conscience community that City Farm relies on for donations. The design process will consist of 1) analysis of the farm’s existing electrical system and its loads, 2) a dive into State, local, and utility rules defining under what circumstances microgrids can be built interconnected to the grid, 3) analysis to estimate optimal component and resource sizing, and 4) recommending locations, components, protection and component settings for the smart distributed energy generation facility 5) analysis on safety, back up capacity, and financial feasibility of the design

design optimization of a distributed energy system through cost and exergy assessments

Abstract In recent years, Distributed Energy Systems (DESs) have been recognized as a good option for sustainable development of future energy systems. With growing environmental concerns, design optimization of DESs through economic assessments only is not sufficient. To achieve long-run sustainability of energy supply, the key idea of this paper is to investigate exergy assessments in DES design optimization to attain rational use of energy resources while considering energy qualities of supply and demand. By using low-temperature sources for low-quality thermal demand, the waste of high-quality energy can be reduced, and the overall exergy efficiency can be increased. Based on a pre-established superstructure, the aim is to determine numbers and sizes of energy devices in the DES and the corresponding operation strategies. A multi-objective linear problem is formulated to reduce the total annual cost and increase the overall exergy efficiency. The Pareto frontier is found to provide different design options for planners based on economic and sustainability priorities, through minimizing a weighted-sum of the total annual cost and primary exergy input, by using branch-and-cut. Numerical results demonstrate that different optimized DES configurations can be found according to the two objectives. Moreover, results also show that the total annual cost and primary exergy input are reduced by 20% - 30% as compared with conventional energy supply systems

Multiport Converter Topologies for Distributed Energy System Applications

In the midst of a present-day global energy renaissance, power electronics has evolved into a top-tier technology discriminator in distributed energy resource (DER) systems. Faced with the formidable task of integrating various types of DER technologies into singular systems, there is a growing appetite for multiport converter (MPC) design. In response, three unique DER MPC topologies are presented: the power sharing converter (PSC), the multi-level nine switch converter (ML9SC), and the modular fuel cell hybrid energy storage (MFC+HES) converter. First, low-voltage and medium-voltage PSC architectures are shown to decouple series-connected source currents and enable independent control. Multidimensional modeling and analysis is then discussed. Next, three system designs are discussed: single-zone, dual-zone, and multi-zone. Each implements PSC technology and high-frequency isolated full-bridge converters to interface multiple fuel cell sources to a medium voltage grid via a single multilevel neutral point clamped inverter interface. A 1-MW simulation and a reduced-scale hardware prototype offer collaborative insight into the inherit benefits of the proposed PSC systems: increased output power, operational flexibility, thermal balancing, source availability, and cost-effectiveness. Secondly, the ML9SC is presented as a component-minimized multi-port converter with low cost, high efficiency, high power quality, and low noise. The multiport characteristic of the ML9SC can be effectively employed in uninterruptible power systems, six-phase wind generators, and doubly-fed induction wind generators. Next, operating constraints and modulation index limits are analyzed at different operating conditions. Loss breakdown is analyzed and compared with the conventional back-to-back multi-level converter. Finally, simulation results are included as proof of concept. Lastly, the proposed MFC+HES converter integrates energy-dense MFC technology with power-dense storage technology. System modularization and hybridization are discussed initially, followed by a selection between supercapacitors and lithium-ion batteries (LIBs). Next, system topology and design is discussed, and the MFC and LIBs are electrically modeled such that Middlebrook’s Extra Element Theorem can mitigate unwanted system resonance and optimize system design. Simulation and hardware results for a 100W MFC+HES system realizes a 300% boost current response capability as well as the following system benefits: limp-home capability, evenly distributed heat/aging, and maximized output power

Optimal Design of Energy System Based on the Forecasting Data with Particle Swarm Optimization

Renewable energy source has developed rapidly and attracted considerable attention. The integration of renewable energy into the energy supply chain requires precise forecast of the output of energy supply chain, thereby reducing energy resource waste and greenhouse gas emissions. In this study, a coupled model system is developed to forecast energy supply chain for the design optimization of distributed energy system, which can be divided into two parts. In the first part, long short-term memory (LSTM) and particle swarm optimization algorithm (PSO) contribute to energy supply chain forecast considering time series, and particle swarm optimization is used to optimize the parameters of the long short-term memory model to improve the forecast accuracy. Results show that the mean absolute error and root mean squared error are 8.7 and 16.3 for the PSO-LSTM model, respectively. In the second part, the forecast results are used as input of the distributed energy system to further optimize the design and operation schemes, so as to achieve the coupling optimization of forecast and design. Finally, a case study is carried out to verify the effectiveness of the proposed method

Reducing carbon footprint of deep-sea oil and gas field exploitation by optimization for Floating Production Storage and Offloading

Deep-sea oil and gas fields are acting as a vital role by providing substantial oil and gas resource, and Floating Production Storage and Offloading is an indispensable tool for the development of offshore oil and gas fields effectively. Here, Life Cycle Assessment is applied to evaluate environmental loads in the whole life cycle of the deep-sea oil and gas production. This paper explores the carbon footprint of Floating Production Storage and Offloading as the time axis. It is found that Floating Production Storage and Offloading is a conceptual product at the design stage and does not generate carbon emission, while the operational stage releases considerable emission by the fuel combustion process, accounting for 88.2% of the entire life cycle. To decrease this part of carbon emission, distributed energy system is considered as a promising choice because it integrates different energy resources and provides an economic and environmental energy allocation scheme to meet the energy demand. For the operation stage, this paper establishes a Multi-objective Mathematical Programming model to determine the selection and capacity of facilities with minimum annual total cost and carbon emissions by considering the energy balance and technical constraints. The model is validated by an example and solved by the weight method. According to designer's demand, distributed energy system can optimize economic objectives in a maximum range of 14.6%, and a maximum emission reduction of 4.53% can be expected compared with the traditional scheme. Sensitivity analysis shows that cost is more sensitive to natural gas price

People-Oriented Perspectives on Designing The Future Energy Market

A report on workshops conducted to explore how Human-Centred Design approaches could help plan the transition to more sustainable Distributed Energy System