Smart grid interoperability use cases for extending electricity storage modeling within the IEC Common Information Model

Copyright @ 2012 IEEEThe IEC Common Information Model (CIM) is recognized as a core standard, supporting electricity transmission system interoperability. Packages of UML classes make up its domain ontology to enable a standardised abstraction of network topology and proprietary power system models. Since the early days of its design, the CIM has grown to reflect the widening scope and detail of utility information use cases as the desire to interoperate between a greater number of systems has increased. The cyber-physical nature of the smart grid places even greater demand upon the CIM to model future scenarios for power system operation and management that are starting to arise. Recent developments of modern electricity networks have begun to implement electricity storage (ES) technologies to provide ancillary balancing services, useful to grid integration of large-scale renewable energy systems. In response to this we investigate modeling of grid-scale electricity storage, by drawing on information use cases for future smart grid operational scenarios at National Grid, the GB Transmission System Operator. We find current structures within the CIM do not accommodate the informational requirements associated with novel ES systems and propose extensions to address this requirement.This study is supported by the UK National Grid and Brunel Universit

Impact of operation strategies of large scale battery systems on distribution grid planning in Germany

Due to the increasing penetration of fluctuating distributed generation electrical grids require reinforcement, in order to secure a grid operation in accordance with given technical specifications. This grid reinforcement often leads to over-dimensioning of the distribution grids. Therefore, traditional and recent advances in distribution grid planning are analysed and possible alternative applications with large scale battery storage systems are reviewed. The review starts with an examination of possible revenue streams along the value chain of the German electricity market. The resulting operation strategies of the two most promising business cases are discussed in detail, and a project overview in which these strategies are applied is presented. Finally, the impact of the operation strategies are assessed with regard to distribution grid planning.Postprint (author's final draft

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles

Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art

Frequency response from aggregated V2G chargers with uncertain EV connections

Fast frequency response (FR) is highly effective at securing frequency dynamics after a generator outage in low inertia systems. Electric vehicles (EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant source of FR in future. However, the uncertainty associated with V2G aggregation, driven by the uncertain number of connected EVs at the time of an outage, has not been fully understood and prevents its participation in the existing service provision framework. To tackle this limitation, this paper, for the first time, incorporates such uncertainty into system frequency dynamics, from which probabilistic nadir and steady state frequency requirements are enforced via a derived moment-based distributionally-robust chance constraint. Field data from over 25,000 chargers is analysed to provide realistic parameters and connection forecasts to examine the value of FR from V2G chargers in annual operation of the GB 2030 system. The case study demonstrates that uncertainty of EV connections can be effectively managed through the proposed scheduling framework, which results in annual savings of Misplaced &6,300 or 37.4 tCO2 per charger. The sensitivity of this value to renewable capacity and FR delays is explored, with V2G capacity shown to be a third as valuable as the same grid battery capacity

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

Power Management of Remote Microgrids Considering Battery Lifetime

Currently, 20% (1.3 billion) of the world’s population still lacks access to electricity and many live in remote areas where connection to the grid is not economical or practical. Remote microgrids could be the solution to the problem because they are designed to provide power for small communities within clearly defined electrical boundaries. Reducing the cost of electricity for remote microgrids can help to increase access to electricity for populations in remote areas and developing countries. The integration of renewable energy and batteries in diesel based microgrids has shown to be effective in reducing fuel consumption. However, the operational cost remains high due to the low lifetime of batteries, which are heavily used to improve the system\u27s efficiency. In microgrid operation, a battery can act as a source to augment the generator or a load to ensure full load operation. In addition, a battery increases the utilization of PV by storing extra energy. However, the battery has a limited energy throughput. Therefore, it is required to provide a balance between fuel consumption and battery lifetime throughput in order to lower the cost of operation. This work presents a two-layer power management system for remote microgrids. The first layer is day ahead scheduling, where power set points of dispatchable resources were calculated. The second layer is real-time dispatch, where schedule set points from the first layer are accepted and resources are dispatched accordingly. A novel scheduling algorithm is proposed for a dispatch layer, which considers the battery lifetime in optimization and is expected to reduce the operational cost of the microgrid. This method is based on a goal programming approach which has the fuel and the battery wear cost as two objectives to achieve. The effectiveness of this method was evaluated through a simulation study of a PV-diesel hybrid microgrid using deterministic and stochastic approach of optimization