A dynamic and fast-changing environment brings challenges for generating long-term scenarios. Although the need to update scenarios for decision-making is recognized, a structured way for executing this process remains unclear. To configure a solution, we propose two concepts that need to be introduced: 1) that scenarios consist of a multi-layered structure, and 2) that changes should be classified according to their impact and uncertainty. Based on this classification, changes are incorporated into the different distinguished layers. To apply these concepts during an update, the paper presents a framework to structurally incorporate new information and uncertainties into scenarios, keeping them up-to-date while guaranteeing that the scenarios remain realistic and useful. The framework is applied to a test case consisting of three scenarios describing the European power market to illustrate how the framework performs in a practical context. Electric Vehicles are chosen as an uncertainty to illustrate how to structurally incorporate changes. Results show that using the framework allows the complexity of the update to be simplified into a step-by-step process. Additionally, it increases transparency by creating a common language for understanding if and how the changing external environment should be incorporated within scenarios.

The paper proposes a probabilistic methodology for minimizing wind spillage and maximizing capacity of the deployed wind generation, whilst improving system reliability. Capacities of the connected wind units are initially determined by using a method developed by the industry. A probabilistic approach is applied for the day-ahead planning to find maximum deployable wind sources so that the prescribed wind spillage is not exceeded. This is done using the optimum power flow, where wind spillages are prioritised with the probabilistic "cost coefficients." Further improvement of wind energy utilization is achieved by installing FACTS devices and making use of real-time thermal ratings. Two ranking lists are developed to prioritize location of SVCs and TCSCs, and they are then combined into a unified method for best FACTS placement. The entire methodology is realized in two sequential Monte Carlo procedures, and the probabilistic results are compared with the state enumeration ones. Results show improved wind utilization, network reliability, and economic aspects.

This paper aims at serving as a critical analysis of Real Options (RO) methodologies that have so far been applied to the flexible evaluation of smart grid developments and as a practical guide to understanding the benefits but more importantly the limitations of RO methodologies. Hence, future research could focus on developing more practical RO tools for application to the energy industry, thus making the utilization of powerful “real options thinking” for decision making under uncertainty more widespread. This is particularly important for applications in low carbon power and energy systems with increasing renewable and sustainable energy resources, given the different types of uncertainty they are facing in the transition towards a truly Smart Grid. In order to do so, and based on an extensive relevant literature review, the analogies with financial options are first presented, with various assumptions and their validity being clearly discussed in order to understand if, when, and how specific methods can be applied. It is then argued how option theory is in most cases not directly applicable to investment in energy systems but requires the consideration of their physical characteristics. The paper finally gives recommendations for building practical RO approaches to energy system (and potentially all engineering) project investments under uncertainty, regardless of the scale, time frame, or type of uncertainty involved.

Classical deterministic models applied to investment valuation in distribution networks may not be adequate for a range of real-world decision-making scenarios as they effectively ignore the uncertainty found in the most important variables driving network planning (e.g., load growth). As greater uncertainty is expected from growing distributed energy resources in distribution networks, there is an increasing risk of investing in too much or too little network capacity and hence causing the stranding and inefficient use of network assets; these costs are then passed on to the end-user. An alternative emerging solution in the context of smart grid development is to release untapped network capacity through Demand-Side Response (DSR). However, to date there is no approach able to quantify the value of 'smart' DSR solutions against 'conventional' asset-heavy investments. On these premises, this paper presents a general real options framework and a novel probabilistic tool for the economic assessment of DSR for smart distribution network planning under uncertainty, which allows the modeling and comparison of multiple investment strategies, including DSR and capacity reinforcements, based on different cost and risk metrics.In particular the model provides an explicit quantification of the economic value of DSR against alternative investment strategies. Through sensitivity analysis it is able to indicate the maximum price payable for DSR service such that DSR remains economically optimal against these alternatives. The proposed model thus provides Regulators with clear insights for overseeing DSR contractual arrangements. Further it highlights that differences exist in the economic perspective of the regulated DNO business and of customers. Our proposed model is therefore capable of highlighting instances where a particular investment strategy is favorable to the DNO but not to its customers, or vice-versa, and thus aspects of the regulatory framework which may need altering.The case study results indicate that DSR can be an economical option to delay or even avoid large irreversible capacity investments, thus reducing overall costs for networks and end customers. However, in order for the value and benefits of DSR to be acknowledged, a change in the regulatory framework (currently based on deterministic analysis) that takes explicit account of uncertainty in planning, as suggested by our work, is required. (C) 2016 The Authors. Published by Elsevier Ltd.

This paper aims to present a probabilistic assessment, via a two-stage stochastic optimization, of the potential benefits from cost optimizing the interaction of thermal and electrical systems of an aggregation of buildings, through the use of domestic electric heat pumps. As more and more intermittent generation is integrated into the grid, while capacity margins are shrinking, not only might prices drop on average but they could also become much more volatile than at present. Potential future day-ahead price scenarios are therefore analyzed in this paper, by using specific price evolution stochastic models developed to take into account different levels of average prices and volatility. Results suggest that lower average values of day-ahead prices combined with very high volatility can lead to noticeable economic benefits from managing EHP aggregation to exploit potential arbitrage opportunities.

This paper aims to present a probabilistic assessment of aggregated demand response ( DR) under different market price conditions for both day-ahead and balancing prices. Different market scenarios are simulated using specific price evolution stochastic models developed to take into account different levels of price volatility and price spikes. The value of a portfolio of Demand Response ( DR) customers under operational uncertainties is assessed through a Real Options ( RO) approach where DR is considered as RO contracts allowing an aggregator seeking to maximise its revenue to sell flexible demand in the day-ahead and balancing markets. Numerical case studies demonstrate the sensitivity of the DR aggregator's profits to the physical characteristics of load payback and to changes in market price volatility and spikes.

This paper aims to set up a probabilistic framework to assess the value of a portfolio of demand response (DR) customers under both operational (short-term) and planning (long-term) uncertainties through real options (ROs) modeling borrowed from financial theory. In an operational setting, DR is considered as an RO contract allowing an aggregator seeking to maximize its revenue to sell flexible demand in the day-ahead market and balance its energy portfolio in the balancing markets. Sequential Monte Carlo simulations (SMCS) are used to value DR activation decisions based on market price evolutions. These decisions combine DR physical characteristics and portfolio scheduling optimization, whereby the aggregator chooses to exercise only the contracts probabilistically leading to a profit, also considering the physical payback effects of load recovery. Sensitivity of profits to changing market conditions and payback characteristics is also assessed. In an investment setting, subject to long-term uncertainties, the value of an investment in DR-enabling technology is quantified through the Datar-Mathews RO approach that applies hybrid SMCS and scenario analysis. The results show how the flexibility value of DR can be highlighted by modeling it as RO, particularly in high volatile markets, and how realistic inclusion of payback characteristics significantly decrease the benefits estimated for DR. In addition, the proposed RO framework generally allows hedging of the risks incurred under long-term and short-term uncertainties.

This paper discusses a new algorithm and defines the functionality required for developing a short-term load-forecasting module for demand response applications. Feedforward artificial neural network (ANN) algorithms are used to provide high forecasting performance when dealing with nonlinear and multivariate problems involving large datasets. The approach is thus suitable for short-term load prediction for disaggregated sites to optimize the demand response process when the data relating to the operating regime or load characteristics of the individual devices and loads connected are unavailable. A detailed description of the relevant external data needed for the forecast is explained. In particular, the algorithm considers weather data for the corresponding time period. The model is tested on data from actual ground source heat pump (GSHP) and heating, ventilation and air conditioning (HVAC) loads of various non-residential buildings at several real sites in the United Kingdom (U.K.). The sensitivity of the parameters of the algorithm, including the number of hidden layers used, is also researched. The proposed algorithm is tested against a linear regression and proves to outperform the latter in all cases. The performance of the algorithm is quantitatively assessed using mean absolute per cent error and mean absolute error metrics. Further analysis plots a comparison of actual and forecasted loads and R-values to determine forecast accuracy.