A unit commitment study of the application of energy storage toward the integration of renewable generation

To examine the potential benefits of energy storage in the electric grid, a generalized unit commitment model of thermal generating units and energy storage facilities is developed. Three different storage scenarios were tested—two without limits to total storage assignment and one with a constrained maximum storage portfolio. Given a generation fleet based on the City of Austin’s renewable energy deployment plans, results from the unlimited energy storage deployment scenarios studied show that if capital costs are ignored, large quantities of seasonal storage are preferred. This operational approach enables storage of plentiful wind generation during winter months that can then be dispatched during high cost peak periods in the summer. These two scenarios yielded $70 million and$94 million in yearly operational cost savings but would cost hundreds of billions to implement. Conversely, yearly cost reductions of $40 million can be achieved with one compressed air energy storage facility and a small set of electrochemical storage devices totaling 13GWh of capacity. Similarly sized storage fleets with capital costs, service lifetimes, and financing consistent with these operational cost savings can yield significant operational benefit by avoiding dispatch of expensive peaking generators and improving utilization of renewable generation throughout the year. Further study using a modified unit commitment model can help to clarify optimal storage portfolios, reveal appropriate market participation approaches, and determine the optimal siting of storage within the grid.Mechanical Engineerin

Commitment and Dispatch of Heat and Power Units via Affinely Adjustable Robust Optimization

The joint management of heat and power systems is believed to be key to the integration of renewables into energy systems with a large penetration of district heating. Determining the day-ahead unit commitment and production schedules for these systems is an optimization problem subject to uncertainty stemming from the unpredictability of demand and prices for heat and electricity. Furthermore, owing to the dynamic features of production and heat storage units as well as to the length and granularity of the optimization horizon (e.g., one whole day with hourly resolution), this problem is in essence a multi-stage one. We propose a formulation based on robust optimization where recourse decisions are approximated as linear or piecewise-linear functions of the uncertain parameters. This approach allows for a rigorous modeling of the uncertainty in multi-stage decision-making without compromising computational tractability. We perform an extensive numerical study based on data from the Copenhagen area in Denmark, which highlights important features of the proposed model. Firstly, we illustrate commitment and dispatch choices that increase conservativeness in the robust optimization approach. Secondly, we appraise the gain obtained by switching from linear to piecewise-linear decision rules within robust optimization. Furthermore, we give directions for selecting the parameters defining the uncertainty set (size, budget) and assess the resulting trade-off between average profit and conservativeness of the solution. Finally, we perform a thorough comparison with competing models based on deterministic optimization and stochastic programming.Comment: 31 page

Stochastic optimal generation bid to electricity markets with emissions risk constraints

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the unit's optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERP's effects on the expected profits and the optimal generation bid.Peer ReviewedPostprint (author's final draft

Base-load cycling on a system with significant wind penetration

Certain developments in the electricity sector may result in suboptimal operation of base-load generating units in countries worldwide. Despite the fact they were not designed to operate in a flexible manner, increasing penetration of variable power sources coupled with the deregulation of the electricity sector could lead to these base-load units being shut down or operated at part-load levels more often. This cycling operation would have onerous effects on the components of these units and potentially lead to increased outages and significant costs. This paper shows the serious impact increasing levels of wind power will have on the operation of base-load units. Those base-load units which are not large contributors of primary reserve to the system and have relatively shorter start-up times were found to be the most impacted as wind penetration increases. A sensitivity analysis shows the presence of storage or interconnection on a power system actually exacerbates base-load cycling until very high levels of wind power are reached. Finally, it is shown that if the total cycling costs of the individual base-load units are taken into consideration in the scheduling model, subsequent cycling operation can be reduced.Thermal Power Generation; Wind Power Generation; Pumped Storage Power Generation; Interconnected Power Systems; Power System Modeling; Costs

A diagnostic tool for assessing organisational readiness for complex change

An output from the ANZSOG-funded project \u27Diagnosing readiness: Testing organisational capabilities\u27. This paper outlines the development of a new diagnostic tool to enable organisations to gauge their preparedness for complex change. Abstract Much is made of the best way to manage change, including a large body of work that argues that there is no point in undertaking such programs unless the organisation is actually ready and able to adopt these new ways of working. In this paper we focus, in particular, on the issue of organisations working together in more ‘joined-up’ ways across government – an example of complex change. We contribute to this literature, arguing that in cases of complex change, not only does there need to be readiness in terms of the change itself, but that there also needs to be readiness in the capacity of the organisation to work together, both within and across organisations. The paper outlines the development of a new diagnostic tool that combines macro and micro levels of analysis in order to enable organisations to gauge their preparedness for complex change

Renewable Electric Energy Integration: Quantifying the Value of Design of Markets for International Transmission Capacity

Integrating large quantities of supply-driven renewable electricity generation remains a political and operational challenge. One of the main obstacles in Europe to installing at least 200 GWs of power from variable renewable sources is how to deal with the insufficient network capacity and the congestion that will result from new flow patterns. We model the current methodology for controlling congestion at international borders and compare its results, under varying penetrations of wind power, with a model that simulates an integrated European network that utilises nodal/localised marginal pricing. The nodal pricing simulations illustrate that congestion - and price - patterns vary considerably between wind scenarios and within countries, and that a nodal price regime could make fuller use of existing EU network capacity, introducing substantial operational cost savings and reducing marginal power prices in the majority of European countries.Power market design, renewable power integration, congestion management, transmission economics

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

Natural reforestation of European mountain landscapes raises major environmental and societal issues. With local stakeholders in the Pyrenees National Park area (France), we studied agricultural landscape colonisation by ash (Fraxinus excelsior) to enlighten its impacts on biodiversity and other landscape functions of importance for the valley socio-economics. The study comprised an integrated assessment of land-use and land-cover change (LUCC) since the 1950s, and a scenario analysis of alternative future policy. We combined knowledge and methods from landscape ecology, land change and agricultural sciences, and a set of coordinated field studies to capture interactions and feedback in the local landscape/land-use system. Our results elicited the hierarchically-nested relationships between social and ecological processes. Agricultural change played a preeminent role in the spatial and temporal patterns of LUCC. Landscape colonisation by ash at the parcel level of organisation was merely controlled by grassland management, and in fact depended on the farmer's land management at the whole-farm level. LUCC patterns at the landscape level depended to a great extent on interactions between farm household behaviours and the spatial arrangement of landholdings within the landscape mosaic. Our results stressed the need to represent the local SES function at a fine scale to adequately capture scenarios of change in landscape functions. These findings orientated our modelling choices in the building an agent-based model for LUCC simulation (SMASH - Spatialized Multi-Agent System of landscape colonization by ASH). We discuss our method and results with reference to topical issues in interdisciplinary research into the sustainability of multifunctional landscapes

Reconsidering UK Community Development Finance

The report includes ten case studies of community finance initiatives in the US and the EU. These highlight the different features of community finance organisations, their target group and their core activities in order to identify what aspects of their operation are integral to creating successful interventions in deprived communities. The objective of these case studies is to highlight particular aspects of their activities and operating environment that are instructive for CDF in the UK.Woodstock Institute provided case studies of US CDFIs