Planning for the impact of distributed solar energy on the grid

The behaviour of single installations of solar energy systems is well understood; however, what happens at an aggregated location, such as a distribution substation, when output of groups of installations cumulate is not so well understood. This paper considers groups of installations attached to distributions substations on which the load is primarily commercial and industrial. Agent-based modelling has been used to model the physical electrical distribution system and the behaviour of equipment outputs towards the consumer end of the network. The paper reports the approach used to simulate both the electricity consumption of groups of consumers and the output of solar systems subject to weather variability with the inclusion of cloud data from the Bureau of Meteorology (BOM). The data sets currently used are for Townsville, North Queensland. The initial characteristics that indicate whether solar installations are cost effective from an electricity distribution perspective are discussed.

Occupant comfort, the housing industry and electricity infrastructure : understanding the synergies

Despite increasingly stringent energy performance regulations for new homes, southeast Queensland has a high and growing penetration of, and reliance on, air conditioners to provide thermal comfort to housing inhabitants. This reliance impacts on electricity infrastructure investment which is the key driving force behind rising electricity prices. This paper reports initial findings of a research project that seeks to better understand three key issues: (i) how families manage their thermal comfort in summer and how well their homes limit overheating; (ii) the extent to which the homes have been constructed according to the building approval documentation; and (iii) the impact that these issues have on urban design, especially in relation to electricity infrastructure in urban developments.

Modelling for the electricity distribution network

Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Optimisation techniques for planning automatic under frequency load shedding in New Zealand’s power system

Electricity generation is vital in developed countries to power the many mechanical and electrical devices that people require. Unfortunately electricity generation is costly. Though electricity can be generated it cannot be stored efficiently. Electricity generation is also difficult to manage because exact demand is unknown from one instant to the next. A number of services are required to manage fluctuations in electricity demand, and to protect the system when frequency falls too low. A current approach is called automatic under frequency load shedding (AUFLS). This article proposes new methods for optimising AUFLS in New Zealand’s power system. The core ideas were developed during the 2015 Maths and Industry Study Group (MISG) in Brisbane, Australia. The problem has been motivated by Transpower Limited, a company that manages New Zealand’s power system and transports bulk electricity from where it is generated to where it is needed. The approaches developed in this article can be used in electrical power systems anywhere in the world.

Decentralized Sustainable Energy Planning of Tumkur District, India

Present work shows the feasibility of decentralized energy options for the Tumkur district in India. Decentralized energy planning (DEP) involves scaling down energy planning to subnational or regional scales. The important aspect of the energy planning at decentralized level would be to prepare an area-based DEP to meet energy needs and development of alternate energy sources at least-cost to the economy and environment. The geographical coverage and scale reflects the level at which the analysis takes place, which is an important factor in determining the structure of models. In the present work, DEP modeling under different scenarios has been carried out for Tumkur district of India for the year 2020. DEP model is suitably scaled for obtaining the optimal mix of energy resources and technologies using a computer-based goal programming technique. The rural areas of the Tumkur district have different energy needs. Results show that electricity needs can be met by biomass gasifier technology, using biomass feedstock produced by allocating only 12% of the wasteland in the district at 8 t/ha/yr of biomass productivity. Surplus electricity can be produced by adopting the option of biomass power generation from energy plantations. The surplus electricity generated can be supplied to the grid. The sustainable development scenario is a least cost scenario apart from promoting self-reliance, local employment, and environmental benefits. (C) 2010 American Institute of Chemical Engineers Environ Prog, 30: 248-258, 2011

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.

Enhanced Operation of Electricity Distribution Grids Through Smart Metering PLC Network Monitoring, Analysis and Grid Conditioning

Low Voltage (LV) electricity distribution grid operations can be improved through a combination of new smart metering systems' capabilities based on real time Power Line Communications (PLC) and LV grid topology mapping. This paper presents two novel contributions. The first one is a new methodology developed for smart metering PLC network monitoring and analysis. It can be used to obtain relevant information from the grid, thus adding value to existing smart metering deployments and facilitating utility operational activities. A second contribution describes grid conditioning used to obtain LV feeder and phase identification of all connected smart electric meters. Real time availability of such information may help utilities with grid planning, fault location and a more accurate point of supply management.

A strategic review of electricity systems models

Electricity systems models are software tools used to manage electricity demand and the electricity systems, to trade electricity and for generation expansion planning purposes. Various portfolios and scenarios are modelled in order to compare the effects of decision making in policy and on business development plans in electricity systems so as to best advise governments and industry on the least cost economic and environmental approach to electricity supply, while maintaining a secure supply of sufficient quality electricity. The modelling techniques developed to study vertically integrated state monopolies are now applied in liberalised markets where the issues and constraints are more complex. This paper reviews the changing role of electricity systems modelling in a strategic manner, focussing on the modelling response to key developments, the move away from monopoly towards liberalised market regimes and the increasing complexity brought about by policy targets for renewable energy and emissions. The paper provides an overview of electricity systems modelling techniques, discusses a number of key proprietary electricity systems models used in the USA and Europe and provides an information resource to the electricity analyst not currently readily available in the literature on the choice of model to investigate different aspects of the electricity system.

Analysis of electric vehicle charging using the traditional generation expansion planning analysis tool WASP-IV

Electric vehicles (EV) are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems. Optimal benefits can only be achieved, if EVs are deployed effectively, so that the exhaust emissions are not substituted by additional emissions in the electricity sector, which can be implemented using Smart Grid controls. This research presents the results of an EV roll-out in the all island grid (AIG) in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV (WASP-IV) tool to measure carbon dioxide emissions and changes in total energy. The model incorporates all generators and operational requirements while meeting environmental emissions, fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch. In the study three distinct scenarios are investigated base case, peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025.

A methodology to analyse the impact of offshore wind forecasting error on electricity markets

Currently wind power is dominated by onshore wind farms in the British Isles, but both the United Kingdom and the Republic of Ireland have high renewable energy targets, expected to come mostly from wind power. However, as the demand for wind power grows to ensure security of energy supply, as a potentially cheaper alternative to fossil fuels and to meet greenhouse gas emissions reduction targets offshore wind power will grow rapidly as the availability of suitable onshore sites decrease. However, wind is variable and stochastic by nature and thus difficult to schedule. In order to plan for these uncertainties market operators use wind forecasting tools, reserve plant and ancillary service agreements. Onshore wind power forecasting techniques have improved dramatically and continue to advance, but offshore wind power forecasting is more difficult due to limited datasets and knowledge. So as the amount of offshore wind power increases in the British Isles robust forecasting and planning techniques are even more critical. This paper presents a methodology to investigate the impacts of better offshore wind forecasting on the operation and management of the single wholesale electricity market in the Republic of Ireland and Northern Ireland using PLEXOS for Power Systems. © 2013 IEEE.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Defining Electricity Tariffs Using the Knowledge About the Consumers Profiles in ELECON Project

The increasing importance of the integration of distributed generation and demand response in the power systems operation and planning, namely at lower voltage levels of distribution networks and in the competitive environment of electricity markets, leads us to the concept of smart grids. In both traditional and smart grid operation, non-technical losses are a great economic concern, which can be addressed. In this context, the ELECON project addresses the use of demand response contributions to the identification of non-technical losses. The present paper proposes a methodology to be used by Virtual Power Players (VPPs), which are entities able to aggregate distributed small-size resources, aiming to define the best electricity tariffs for several, clusters of consumers. A case study based on real consumption data demonstrates the application of the proposed methodology.