Analysis of Large Scale PV Systems with Energy Storage to a Utility Grid

With electric distribution network operators experiencing an exponential increase in distributed energy resource connections to the power grid, operational challenges arise attributable to the traditional methods of building distribution feeders. Photovoltaic (PV) solar systems are the major contributor due to recent technological advancements. Though this renewable energy resource is beneficial to human society, unfavorable electrical conditions can arise from the inherit variability of solar energy. Extreme variability of power injection can force excessive operations of voltage regulation equipment and potentially degrade customer voltage quality. If managed and controlled properly, battery energy storage systems installed on a distribution feeder have the ability to compliment solar generation and dampen the negative effects of solar generation. Now that customers are connecting their own generation, the traditional design assumption of load flowing from substation to customer is nullified. This research aims first to capture the maximum amount of generation that can be connected to a distribution feeder. Numerous deployments of generation scenarios are applied on six unique distribution feeders to conclude that hosting capacity is dependent on interconnect location. Then, existing controllers installed on voltage regulation equipment are modeled in detail. High resolution time series analysis driven from historical measurements is conducted on two contrasting feeders with specific PV generator deployments. With the proper modeling of on-load tap changer controls, excessive operations caused by extreme PV generation swings were captured. Several services that battery energy storage systems can provide when connected to an individual distribution feeder with significant PV generation include long term absorption of excessive PV generation, dynamic response to extreme PV generation ramping, and release of stored energy for system peak shaving. A centralized master energy coordinator is proposed with the ability to dispatch the battery system in such a fashion to implement each service throughout consecutive days of operation. This solution was built by integrating load and solar energy forecasting predictions in order to construct an optimum charging and discharging schedule that would maximize the asset’s lifespan. Multiple load and solar generation scenarios including a consecutive three day run is included to verify the robustness of this energy coordinator

Net solar generation potential from urban rooftops in Los Angeles

Rooftops provide accessible locations for solar energy installations. While rooftop solar arrays can offset in-building electricity needs, they may also stress electric grid operations. Here we present an analysis of net electricity generation potential from distributed rooftop solar in Los Angeles. We integrate spatial and temporal data for property-level electricity demands, rooftop solar generation potential, and grid capacity constraints to estimate the potential for solar to meet on-site demands and supply net exports to the electric grid. In the study area with 1.2 million parcels, rooftop solar could meet 7200 Gigawatt Hours (GWh) of on-site building demands (~29% of demand). Overall potential net generation is negative, meaning buildings use more electricity than they can produce. Yet, cumulative net export potential from solar to grid circuits is 16,400 GWh. Current policies that regulate solar array interconnection to the grid result in unutilized solar power output of 1700 MW. Lower-income and at-risk communities in LA have greater potential for exporting net solar generation to the grid. This potential should be recognized through investments and policy innovations. The method demonstrates the need for considering time-dependent calculations of net solar potential and offers a template for distributed renewable energy planning in cities

Challenges to the Integration of Renewable Resources at High System Penetration

Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches

ASEAN grid flexibility: Preparedness for grid integration of renewable energy

In 2015, ASEAN established a goal of increasing its renewable energy share in its energy portfolio from approximately 13–23% by 2025. Renewable electricity, especially intermittent and variable sources, presents challenges for grid operators due to the uncertain timing and quantity of electricity supply. Grid flexibility, the electric grid's ability to respond to changing demands and supply, now stands a key resource in responding to these uncertainties while maximizing the cost-effective role of clean energy. We develop and apply a grid flexibility assessment tool to assess ASEAN's current grid flexibility using six quantitative indicators: grid reliability, electricity market access; load profile ramp capacity; quality of forecasting tools; proportion of electricity generation from natural gas; and renewable energy diversity. We find that ASEAN nations cluster into three groups: better; moderately; and the least prepared nations. We develop an analytical ramp rate calculator to quantify expected load ramps for ASEAN in an integrated ASEAN Power Grid scenario. The lack of forecasting systems and limited electricity market access represent key weaknesses and areas where dramatic improvements can become cost-effective means to increase regional grid flexibility. As ASEAN pursues renewable energy targets, regional cooperation remains essential to address identified challenges. Member nations need to increase grid flexibility capacity to adequately prepare for higher penetrations of renewable electricity and lower overall system costs

ECONOMIC Potential of Renewable Energy in Vietnam's Power Sector

A bottom-up Integrated Resource Planning model is used to examine the economic potential of renewable energy in Vietnam’s power sector. In a baseline scenario without renewables, coal provides 44% of electricity generated from 2010 to 2030. The use of renewables could reduce that figure to 39%, as well as decrease the sector’s cumulative emission of CO2 by 8%, SO2 by 3%, and NOx by 4%. In addition,renewables could avoid installing 4.4GW in fossil fuel generating capacity, conserve domestic coal,decrease coal and gases imports, improving energy independence and security. Wind could become cost-competitive assuming high but plausible on fossil fuel prices, if the cost of the technology falls to 900 US$/kW

Final report: Workshop on: Integrating electric mobility systems with the grid infrastructure

EXECUTIVE SUMMARY: This document is a report on the workshop entitled “Integrating Electric Mobility Systems with the Grid Infrastructure” which was held at Boston University on November 6-7 with the sponsorship of the Sloan Foundation. Its objective was to bring together researchers and technical leaders from academia, industry, and government in order to set a short and longterm research agenda regarding the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. The report is a summary of their insights based on workshop presentations and discussions. The list of participants and detailed Workshop program are provided in Appendices 1 and 2. Public and private decisions made in the coming decade will direct profound changes in the way people and goods are moved and the ability of clean energy sources – primarily delivered in the form of electricity – to power these new systems. Decisions need to be made quickly because of rapid advances in technology, and the growing recognition that meeting climate goals requires rapid and dramatic action. The blunt fact is, however, that the pace of innovation, and the range of business models that can be built around these innovations, has grown at a rate that has outstripped our ability to clearly understand the choices that must be made or estimate the consequences of these choices. The group of people assembled for this Workshop are uniquely qualified to understand the options that are opening both in the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. They were asked both to explain what is known about the choices we face and to define the research issues most urgently needed to help public and private decision-makers choose wisely. This report is a summary of their insights based on workshop presentations and discussions. New communication and data analysis tools have profoundly changed the definition of what is technologically possible. Cell phones have put powerful computers, communication devices, and position locators into the pockets and purses of most Americans making it possible for Uber, Lyft and other Transportation Network Companies to deliver on-demand mobility services. But these technologies, as well as technologies for pricing access to congested roads, also open many other possibilities for shared mobility services – both public and private – that could cut costs and travel time by reducing congestion. Options would be greatly expanded if fully autonomous vehicles become available. These new business models would also affect options for charging electric vehicles. It is unclear, however, how to optimize charging (minimizing congestion on the electric grid) without increasing congestion on the roads or creating significant problems for the power system that supports such charging capacity. With so much in flux, many uncertainties cloud our vision of the future. The way new mobility services will reshape the number, length of trips, and the choice of electric vehicle charging systems and constraints on charging, and many other important behavioral issues are critical to this future but remain largely unknown. The challenge at hand is to define plausible future structures of electric grids and mobility systems, and anticipate the direct and indirect impacts of the changes involved. These insights can provide tools essential for effective private ... [TRUNCATED]Workshop funded by the Alfred P. Sloan Foundatio

Integrated Generation Management for Maximizing Renewable Resource Utilization

Two proposed methods to reduce the effective intermittency and improve the efficiency of wind power generation in the grid are spatial smoothing of wind generation and utilization of short term electrical storage to deal with lulls in production. In this thesis, based on a concept called integrated generation management (IGM), we explore the impact of spatial smoothing and the use of emerging plug-in hybrid electric vehicles (PHEVs) as a potential storage resource to the smart-grid. IGM combines nuclear, slow load-following coal, fast load-following natural gas, and renewable wind generation with an optimal control method to maximize the renewable generation and minimize the fossil generation. With the increasing penetration of PHEVs, the power grid is seeing new opportunities to make itself smarter than ever by utilizing those relatively large batteries. Based on current projections of PHEV market penetration and various wind generation scenarios, we demonstrate the potential for efficient wind integration at levels of approaching 30% of the aver- age electrical load with utilization efficiency exceeding 65%. At lower levels of integration (e.g. 15%), efficiencies are possible exceeding 85%

CleanTX Analysis on the Smart Grid

The utility industry in the United States has an opportunity to revolutionize its electric grid system by utilizing emerging software, hardware and wireless technologies and renewable energy sources. As electricity generation in the U.S. increases by over 30% from today’s generation of 4,100 Terawatt hours per year to a production of 5,400 Terawatt hours per year by 2030, a new type of grid is necessary to ensure reliable and quality power. The projected U.S. population increase and economic growth will require a grid that can transmit and distribute significantly more power than it does today. Known as a Smart Grid, this system enables two- way transmission of electrons and information to create a demand-response system that will optimize electricity delivery to consumers. This paper outlines the issues with the current grid infrastructure, discusses the economic advantages of the Smart Grid for both consumers and utilities, and examines the emerging technologies that will enable cleaner, more efficient and cost- effective power transmission and consumption.IC2 Institut

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe