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In incentive-based demand response programs, the aggregator sends reward signals to the consumers to stimulate consumption changes for mobilizing their flexibility in energy end-uses, which can then be traded in different markets. Therefore, the amount of flexibility available for being collected and traded depends on reward signals, among other factors. Hence, the aggregator needs to anticipate how the end-users will respond to the reward signals. In the presence of time-of-day tariffs, from the end-users’ perspective, those reward signals can change the impact of the cost per unit of energy in different periods of the day. Thus, the aggregation of residential flexibility requires grasping how the energy price scheme (the prices at which the end-user purchases the energy from the retailer) interacts with the reward signals. In this study, the retailer and the aggregator are separate entities. Therefore, the aggregator does not have direct access to the end-users’ energy pricing data. This paper presents a study to show the impact of different energy pricing schemes on mobilizing the flexibility of a group of residential end-users and the aggregator’s profit. Accordingly, different end-use profiles, a range of reward values, and two different rewarding strategies are considered. Two separate mixed integer linear programing models are proposed, which are associated with the end-user’s and the aggregator’s problems. The objective of the aggregator’s problem is to maximize the profit by identifying which end-users’ flexibility will be selected to form the tradable aggregated flexibility. From the end-users’ perspective, the objectives are to minimize the energy cost and the potential discomfort inflicted by changing the regular operation of appliances. The results show the effectiveness of the proposed methodology to analyze the impact of energy price scheme and rewarding strategies on assembling flexibility and the aggregation profitability.

Energy behaviors have increasing importance in the design of policies and programs that promote transitions to a low-carbon and more sustainable future [...]

Understanding to what extent the emergence of prosumers and prosumagers organized in energy communities can impact the organization and operation of power grids has been one of the major recent research avenues at the European level. In renewable-based communities aiming to reach some level of energy self-sufficiency, a key issue to be addressed is assessing how the presence of end-users playing different roles in the system (self-consuming, producing and trading, performing demand management, etc.) can influence the overall system performance. In this setting, this paper combines Distributed Artificial Intelligence and optimization approaches to assess how prosumagers and consumers pursuing different goals can influence the energy self-sufficiency of a local energy community. The residential demand is accurately modeled, and the agents’ preferences are considered in the modeling to represent a smart community. The results show that although energy community members may have conflicting individual goals, the overall system self-sufficiency can be maximized with economic benefits for all stakeholders, thus illustrating the advantages of energy communities.

Vehicle-to-grid (V2G) technology heralds great promise as a demand-side resource to contribute to more efficient grid management and promote the use of decentralized renewable energy. In this light, we propose a new optimization model for the sustainable energy and reserve market management in renewable-driven microgrid (RMG) plug-in electric vehicles (PEVs) parking lot systems. The RMG is composed of a hybrid photovoltaic/wind/hydrogen energy and storage system, along with local dispatchable generation units and bidirectional grid connection. The RMG is coupled to a smart PEVs parking lot, which is equipped with grid-to-vehicle (G2V) and V2G technologies allowing for not only PEVs aggregation and control but also optimal allocation of energy resources. Time-of-use (TOU) prices are considered in a demand response program (DRP) to enhance both economic and environmental performances by encouraging end-users to shift their energy demands from peak to off-peak time periods. Additionally, the model accounts for an economic incentive to PEVs owners to compensate for battery degradation. The integrated system eco-efficiency is evaluated through the application of the novel life cycle assessment-based Eco-cost indicator. The resulting mixed-integer linear programming model to minimize sustainability costs is implemented in GAMS and solved to global optimality. Different case studies are performed to demonstrate the effectiveness of the proposed modelling approach. Energy analyses results reveal that the optimal G2V-V2G operation, allied to TOU prices in a DRP, and reserve market management can reduce around 42% the energy and environmental costs of the RMG-PEVs parking lot system.

This paper develops a bottom-up approach in the scope of a multi-objective linear programming model (MOLP) based on Input-Output (I-O) analysis to account for investment options aimed at improving the thermal properties of building envelope (e.g., the insulation of external walls and roof, and the replacement of window frames and window glazing). This methodological framework aims at assessing the trade-offs between the overall employment, GDP and energy savings associated with the building sector (residential, private services and public services). Distinct impacts, namely on direct and indirect employment generation, environment (CO2 emissions), energy security supply (energy imports and renewable energy production) and other relevant economic indicators are also analysed. Different sets of input parameters for the economic context and the environmental impacts have been defined as interval coefficients to account for uncertainty. Robust solutions are then obtained by considering the minimisation of the worst possible deviation of the interval objective functions to the corresponding interval ideal solutions.

This paper describes the use of Soft Systems Methodology (SSM) as a tool for problem structuring, which is the first phase encompassed in a methodological approach currently under development to provide decision support based on Multi‐Criteria Decision Analysis (MCDA) in energy planning problems in an urban context. In order to apply the methodology to a real‐world problem, a medium sized Portuguese city has been chosen as the decision setting. SSM is used for characterizing as precisely as possible the decision problem context, identifying the main stakeholders and their relations, and discerning the relevant criteria at stake for each one. Future work directions based on this phase are also envisaged. Santrauka Straipsnyje aprašoma operacinės sistemos metodologija (OSM), kuri bus taikoma kaip daugiakriterinės analizės metodais pagrįsta sprendimų paramos sistema miesto energetikos planavimo problemoms spręsti. Siekiant metodologiją pritaikyti realiame gyvenime, eksperimentui buvo parinktas vidutinio dydžio Portugalijos miestas. Operacines sistemos metodologija taikyta kuo tiksliau nustatant pagrindines problemas, identifikuojant pagrindines suinteresuotas šalis ir jų santykius, nustatant vienas kitam įtaka darančius rodiklius. Numatytos būsimos darbo kryptys. First published online: 10 Feb 2011 Reikšminiai žodžiai: operacinė sistemos metodologija, daugiakriterinė sprendimų analizė, miesto energetikos planavimas

There are advantages in considering the routing problem in integrated communication networks as a multiobjective shortest path problem, having in mind to grasp eventual conflicts and trade-offs among distinct objectives and quality of services (QoS) constraints. On the other hand the utilisation of dynamic routing methods in various types of networks is well known to have significant impact on network performance and cost, namely in overload and failure conditions. This paper presents the detailed formulation of a proposal of a multiple objective dynamic routing method (MODR) of periodic state dependent routing type, enabling to represent distinct QoS related metrics and requirements in a consistent manner. The MODR method present formulation is based on a multiple objective shortest path model with constraints and is prepared to use implied costs as one of the metrics. Alternative paths for each traffic flow are changed as a function of periodic updates of certain QoS related parameters estimated from real time measurements on the routes and trunks of the network. Such paths are computed by a specialised and efficient variant of a bi-objective shortest path constrained algorithm, developed for the MODR, enabling to incorporate flexible requirements on the QoS metrics. The architecture of the routing system is discussed together with the features of its main modules. An illustrative example of application of the MODR path calculation module to a circuit-switched type network using blocking probability and implied cost as metrics, is also presented, considering different overload conditions.