A virtual power plant model for time-driven power flow calculations

This paper presents the implementation of a custom-made virtual power plant model in OpenDSS. The goal is to develop a model adequate for time-driven power flow calculations in distribution systems. The virtual power plant is modeled as the aggregation of renewable generation and energy storage connected to the distribution system through an inverter. The implemented operation mode allows the virtual power plant to act as a single dispatchable generation unit. The case studies presented in the paper demonstrate that the model behaves according to the specified control algorithm and show how it can be incorporated into the solution scheme of a general parallel genetic algorithm in order to obtain the optimal day-ahead dispatch. Simulation results exhibit a clear benefit from the deployment of a virtual power plant when compared to distributed generation based only on renewable intermittent generation.Peer ReviewedPostprint (published version

Smart microgrids and virtual power plants in a hierarchical control structure

In order to achieve a coordinated integration of distributed energy resources in the electrical network, an aggregation of these resources is required. Microgrids and virtual power plants (VPPs) address this issue. Opposed to VPPs, microgrids have the functionality of islanding, for which specific control strategies have been developed. These control strategies are classified under the primary control strategies. Microgrid secondary control deals with other aspects such as resource allocation, economic optimization and voltage profile improvements. When focussing on the control-aspects of DER, VPP coordination is similar with the microgrid secondary control strategy, and thus, operates at a slower time frame as compared to the primary control and can take full advantage of the available communication provided by the overlaying smart grid. Therefore, the feasibility of the microgrid secondary control for application in VPPs is discussed in this paper. A hierarchical control structure is presented in which, firstly, smart microgrids deal with local issues in a primary and secondary control. Secondly, these microgrids are aggregated in a VPP that enables the tertiary control, forming the link with the electricity markets and dealing with issues on a larger scale

Carbon Free Boston: Energy Technical Report

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Transportation Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Offsets Technical Report; Available at http://sites.bu.edu/cfb/INTRODUCTION: The adoption of clean energy in Boston’s buildings and transportation systems will produce sweeping changes in the quantity and composition of the city’s demand for fuel and electricity. The demand for electricity is expected to increase by 2050, while the demand for petroleum-based liquid fuels and natural gas within the city is projected to decline significantly. The city must meet future energy demand with clean energy sources in order to meet its carbon mitigation targets. That clean energy must be procured in a way that supports the City’s goals for economic development, social equity, environmental sustainability, and overall quality of life. This chapter examines the strategies to accomplish these goals. Improved energy efficiency, district energy, and in-boundary generation of clean energy (rooftop PV) will reduce net electric power and natural gas demand substantially, but these measures will not eliminate the need for electricity and gas (or its replacement fuel) delivered into Boston. Broadly speaking, to achieve carbon neutrality by 2050, the city must therefore (1) reduce its use of fossil fuels to heat and cool buildings through cost-effective energy efficiency measures and electrification of building thermal services where feasible; and (2) over time, increase the amount of carbon-free electricity delivered to the city. Reducing energy demand though cost effective energy conservation measures will be necessary to reduce the challenges associated with expanding the electricity delivery system and sustainably sourcing renewable fuels.Published versio

Virtual power plants with electric vehicles

The benefits of integrating aggregated Electric Vehicles (EV) within the Virtual Power Plant (VPP) concept, are addressed. Two types of EV aggregators are identified: i) Electric Vehicle Residential Aggregator (EVRA), which is responsible for the management of dispersed and clustered EVs in a residential area and ii) Electric Vehicle Commercial Aggregator (EVCA), which is responsible for the management of EVs clustered in a single car park. A case study of a workplace EVCA is presented, providing an insight on its operation and service capabilities

Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power

The carbon footprint of desalination: An input-output analysis of seawater reverse osmosis desalination in Australia for 2005–2015

This study examines greenhouse gas emissions for 2005–2015 from seawater desalination in Australia, using conventional energies. We developed a tailor-made multi-regional input-output-model. We complemented macroeconomic top-down data with plant-specific desalination data of the largest 20 desalination plants in Australia. The analysed capacity cumulates to 95% of Australia's overall seawater desalination capacity. We considered the construction and the operation of desalination plants. We measure not only direct effects, but also indirect effects throughout the entire value chain. Our results show the following: We identify the state of Victoria with the highest emissions due to capital and operational expenditures (capex and opex). The contribution of the upstream value chain to total greenhouse gas emissions increases for capex and decreases for opex. For capex, the construction of intake and outfall is the driving factor for carbon emissions. For opex, electricity consumption is the decisive input factor. Both in construction and operation, we identify the critical role of the electricity sector for carbon emissions throughout the supply chain effects. The sector contributes 69% during the zenith of the construction phase and 96% during the operating phase to the entire emissions. We estimate the total emissions for 2015 at 1193 kt CO2e