Foresighted Demand Side Management

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities

WoodCircus, Underpinning the vital role of the forest-based sector in the Circular Bioeconomy. D2.2 Resource Efficiency, Side Streams and Value Chain Analysis – WP2 Final Report

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Integration of Demand Response and photovoltaic resources in residential segments

[EN] The development of renewable sources in residential segments is basic to achieve a sustainable energy scenario in the horizon 2030-2050 because these segments explain around 25% of the final energy consumption. Demand Response and its effective coordination with renewable are additional concerns for residential segments. This paper deals with two problems: the demonstration of cost-effectiveness of renewables in three different scenarios, and the application of the flexibility of demand, performing as energy storage systems, to efficiently manage the generation of renewable sources while improving benefits and avoiding penalties for the customer. A residential customer in Spain has been used as example. The work combines the use of a commercial simulator to obtain photovoltaic generation, the monitoring of customer to obtain demand patterns, and the development of a Physically-Based Model to evaluate the capability of demand to follow self-generation. As a main result, the integration of models (load/generation), neglected in practice in other approaches in the literature, allows customers to improve revenue up to 20% and reach a basic but important knowledge on how they can modify the demand, development of new skills and, in this way, learn how to deal with the characteristics and limitations of both Demand and Generation when a customer becomes a prosumer. This synergy amongst demand and generation physically-based models boosts the possibilities of customers in electricity markets.This work was supported by the Ministerio de Economia, Industria y Competitividad (Spanish Government) under research projects ENE-2016-78509-C3-2-P, ENE-2016-78509-C3-1-P, and FEDER funds. Authors have also received funds from these grants for covering the costs to publish in open access.Garcia-Garre, A.; Gabaldon, A.; Álvarez, C.; Ruiz-Abellon, MDC.; Guillamon, A. (2018). Integration of Demand Response and photovoltaic resources in residential segments. Sustainability. 10(9):1-31. https://doi.org/10.3390/su10093030S13110

The Role of Self-Reinforcing Mechanisms in Organizational Adaptation: Evidence from German Utilities

This dissertation enhances existing understanding of the role of self-reinforcing mechanisms as driving forces of organizational path dependence and thus limiting factors for organizational adaptation. In this way, the dissertation sheds light on the underlying dynamics of scale, complementary, learning, coordination, and expectation effects that keep organizations on a once entered development path. To investigate the specific development of six German utility companies between the liberalization of the German energy market in 1999, and 2015, this dissertation applies a multiple-case study approach to empirically uncover the self-reinforcing mechanisms’ modes of action in replicating existing activity patterns and thus shaping firms’ development paths. Thereby, this dissertation contributes to the understanding of self-reinforcing mechanisms in three respects. First, it advances understanding of the underlying dynamics of self-reinforcing mechanisms by adding new dimensions to conceptions of learning, coordination, and expectation effects and providing in-depth explanations for their stabilizing effects. Second, this dissertation enhances a differentiated view on self-reinforcing mechanisms while offering empirical evidence that these effects not only have a limiting influence but might also facilitate organizational adaptation in certain contextual settings. Third, this dissertation contributes to an understanding of the role of managerial agency while empirically substantiating that agency matters, even in a state of path dependence. Accordingly, this dissertation proposes a reconceptualization of the classic theory of organizational path dependence in a less deterministic manner, placing greater emphasis on the role and influence of corporate actors in breaking existing paths. Indeed, this dissertation strongly suggests that the driving forces of path dependence should be understood as temporal influencing factors on firms’ strategic initiatives that appear to have either a widening or a limiting effect on the scope of alternatives, and which can consciously be overcome. Besides its contributions to theory, this dissertation provides concrete practical guidance for managers to increase their awareness and to counteract those stabilizing influencing factors in the context of strategic decision making

Sustainable Consumption and Production Patterns: Policy Design and Evaluation

This book is intended to highlight why SCP policy design and evaluation needs to overcome conventional environmental policy framework. Emerging SCP policy design and evaluation do not involve focusing on individual products or behaviors or improving efficiency in management systems in relation to environmental sustainability; instead, they address more socio-economic systems and target collective efforts for transition. Effort has been made for this book/Special Issue to feature studies contributing to policy design and evaluation in this direction. It contains 11 papers covering challenges and opportunities for SCP policy design, application of foresight to policy design, evaluation of NDC potentials to facilitate sustainable lifestyles, comparative analysis of sustainable development criteria, sustainable lifestyle and education, subjective wellbeing and sustainable consumption, case studies on challenges and opportunities for sustainability transition at the local and community level, and three case studies on how to fill gaps between policy goals and environmental behavior at a city level in China, Vietnam, and Thailand. The papers in this book suggest that SCP policy design and evaluation need to pay more attention to social aspects of sustainability such as social infrastructure and well-being and socio-technical systems to ensure effective and just transition to sustainability

Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Transactive Control of Coupled Electric Power and District Heating Networks

The aim to decarbonize the energy supply represents a major technical and social challenge. The design of approaches for future energy network operation faces the technical challenge of needing to coordinate a vast number of new network participants spatially and temporally, in order to balance energy supply and demand, while achieving secure network operation. At the same time these approaches should ideally provide economic optimal solutions. In order to meet this challenge, the research field of transactive control emerged, which is based on an appropriate interaction of market and control mechanisms. These approaches have been extensively studied for electric power networks. In order to account for the strong differences between the operation of electric power networks and other energy networks, new approaches need to be developed. Therefore, within this work a new transactive control approach for Coupled Electric Power and District Heating Networks (CEPDHNs) is presented. As this is built upon a model-based control approach, a suitable model is designed first, which enables to operate coupled electric power and district heating networks as efficient as possible. Also, for the transactive control approach a new fitted procedure is developed to determine market clearing prices in the multi-energy system. Further, a distributed form of district heating network operation is designed in this context. The effectiveness of the presented approach is analyzed in multiple simulations, based on real world networks

Advanced Mechanism Design for Electric Vehicle Charging Scheduling in the Smart Infrastructure

Electric vehicle (EV) continues to grow rapidly due to low emission and high intelligence. This thesis considers a smart infrastructure (SI) as an EV-centered ecosystem, which is an integrated and connected multi-modal network involving interacting intelligent agents, such as EVs, charging facilities, electric power grids, distributed energy resources, etc. The system modeling paradigm is derived from distributed artificial intelligence and modelled as multi-agent systems (MAS), where the agents are self-interested and reacting strategically to maximize their own benefits. The integration, interaction, and coordination of EVs with SI components will raise various features and challenges on the transportation efficiency, power system stability, and user satisfaction, as well as opportunities provided by optimization, economics, and control theories, and other advanced technologies to engage more proactively and efficiently in allocating the limited charging resources and collaborative decision-making in a market environment. A core challenge in such an EV ecosystem is to trade-off the two objectives of the smart infrastructure, of system-wide efficiency and at the same time the social welfare and individual well-being against agents’ selfishness and collective behaviors. In light of this, scheduling EVs' charging activities is of great importance to ensure an efficient operation of the smart infrastructure and provide economical and satisfactory charging experiences to EV users under the support of two-way flow of information and energy of charging facilities. In this thesis, we develop an advanced mechanism design framework to optimize the charging resource allocation and automate the interaction process across the overall system. The key innovation is to design specific market-based mechanisms and interaction rules, integrated with concepts and principles of mechanism design, scheduling theory, optimization theory, and reinforcement learning, for charging scheduling and dynamic pricing problem in various market structures. Specifically, this research incorporates three synergistic areas: (1) Mathematical modelling for EV charging scheduling. We have developed various mixed-integer linear programs for single-charge with single station, single-charge with multiple stations, and multi-charge with multiple stations in urban or highway environments. (2) Market-based mechanism design. Based on the proposed mathematical models, we have developed particular market-based mechanisms from the resource provider’s prospective, including iterative bidding auction, incentive-compatible auction, and simultaneous multi-round auction. These proposed auctions contain bids, winner determination models, and bidding procedure, with which the designer can compute high quality schedules and preserve users’ privacy by progressively eliciting their preference information as necessary. (3) Reinforcement learning-based mechanism design. We also proposed a reinforcement mechanism design framework for dynamic pricing-based demand response, which determines the optimal charging prices over a sequence of time considering EV users’ private utility functions. The learning-based mechanism design has effectively improved the long-term revenue despite highly-uncertain requests and partially-known individual preferences of users. This Ph.D. dissertation presents a market prospective and unlocks economic opportunities for MAS optimization with applications to EV charging related problems; furthermore, applies AI techniques to facilitate the evolution from manual mechanism design to automated and data-driven mechanism design when gathering, distributing, storing, and mining data and state information in SI. The proposed advanced mechanism design framework will provide various collaboration opportunities with the research expertise of reinforcement learning with innovative collective intelligence and interaction rules in game theory and optimization tools, as well as offers research thrust to more complex interfaces in intelligent transportation system, smart grid, and smart city environments

Adopting Circular Economy Current Practices and Future Perspectives

The development of a closed-loop cycle is a necessary condition so as to develop a circular economy model as an alternative to the linear model, in order to maintain the value of products and materials for as long as possible. For this motive, the definition of the value must be demonstrated for both the environment and the economy. The presence of these analyses should be associated with the social dimension and the human component. A strong cooperation between social and technical profiles is a new challenge for all researchers. End of life of products attract a lot of attention, and the final output could be the production of technologies suitable for managing this waste