Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated

デマンドサイドマネジメントの省エネルギー技術と電力市場の最適分析に関する研究

This study analyzed the policy effect of demand side management (DSM) and the preference of energy system selection from the technical side and the economic side. The results showed that the promotion and performance improvement of microgrid and air conditioning system are most suitable for the development of DSM. At the same time, the liberalization of electricity market is helpful to the promotion of demand side technology. The research studied the effect of Japan’s “top runner” policy on equipment energy efficiency improvement and analyzed the rebound effect of carbon emissions in the whole life cycle in chapter 4. In chapter 5, the adaptability of different types of buildings under different demand side liberalization degrees was compared. In chapter 6, the correlation analysis of electricity price, the short-term forecast and the influence of different electricity price modes on technical side means were analyzed.北九州市立大

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method

Interconnection of solar home systems as a path to bottom-up electrification

Solar Home Systems (SHSs) have revolutionised electricity access for off grid communities, but have a number of significant limitations. They have limited demand diversity, produce excess energy and lack a clear pathway to scale alongside growing energy demand. Electrical interconnection of existing installed SHSs to create minigrids could offer a way to both scale up energy demand and make use of wasted energy. This bottom-up approach has the potential to be flexible to the changing needs of communities, by using SHSs as a starting point for wider electrification, rather than the end goal. Despite this potential, little analytical work has been undertaken to model SHS interconnection, particularly accounting for demand diversity and long-term system performance. This thesis presents a time sequential stochastic model of interconnected SHSs, to investigate these systems under multi-year operational timescales at high temporal resolution. It is shown for case study systems based on real SHS topologies that there exists significant demand diversity, with small clusters of 20 houses with identical appliances exhibiting an average peak demand of less than 70% of the combined worst-case peak for individual SHSs. Excess generated energy is shown to be an average of 100 Wh a day for the smaller system types and 1000Wh a day for larger systems. Interconnection of these systems demonstrates a significant reductions in LCOE for all system types compared to islanded operation, through more optimal dispatch of battery storage assets and use of excess energy. This resulted in a final LCOE of $0.63/kWh for a network of 12 large SHSs - a reduction of 48.12$0.703/kWh for a network of 12 small SHSs - a reduction of 55.23% compared to islanded operation. This informed an investigation of possible operational business models for a network of SHSs, with three approaches proposed - an Energy System Operator with direct control over all users’ systems, an Aggregator model, where the system operator facilitates an energy market and a Peer-to-Peer model with direct consumer to consumer energy trading. This thesis provides a robust evidence base for SHS interconnection – demonstrating that the approach can lower cost of energy and facilitate demand growth for off grid energy consumers and proposes appropriate business models to deliver this affordable and clean energy.Solar Home Systems (SHSs) have revolutionised electricity access for off grid communities, but have a number of significant limitations. They have limited demand diversity, produce excess energy and lack a clear pathway to scale alongside growing energy demand. Electrical interconnection of existing installed SHSs to create minigrids could offer a way to both scale up energy demand and make use of wasted energy. This bottom-up approach has the potential to be flexible to the changing needs of communities, by using SHSs as a starting point for wider electrification, rather than the end goal. Despite this potential, little analytical work has been undertaken to model SHS interconnection, particularly accounting for demand diversity and long-term system performance. This thesis presents a time sequential stochastic model of interconnected SHSs, to investigate these systems under multi-year operational timescales at high temporal resolution. It is shown for case study systems based on real SHS topologies that there exists significant demand diversity, with small clusters of 20 houses with identical appliances exhibiting an average peak demand of less than 70% of the combined worst-case peak for individual SHSs. Excess generated energy is shown to be an average of 100 Wh a day for the smaller system types and 1000Wh a day for larger systems. Interconnection of these systems demonstrates a significant reductions in LCOE for all system types compared to islanded operation, through more optimal dispatch of battery storage assets and use of excess energy. This resulted in a final LCOE of $0.63/kWh for a network of 12 large SHSs - a reduction of 48.12$0.703/kWh for a network of 12 small SHSs - a reduction of 55.23% compared to islanded operation. This informed an investigation of possible operational business models for a network of SHSs, with three approaches proposed - an Energy System Operator with direct control over all users’ systems, an Aggregator model, where the system operator facilitates an energy market and a Peer-to-Peer model with direct consumer to consumer energy trading. This thesis provides a robust evidence base for SHS interconnection – demonstrating that the approach can lower cost of energy and facilitate demand growth for off grid energy consumers and proposes appropriate business models to deliver this affordable and clean energy

Engineering Local Electricity Markets for Residential Communities

In line with the progressing decentralization of electricity generation, local electricity markets (LEMs) support electricity end customers in becoming active market participants instead of passive price takers. They provide a market platform for trading locally generated (renewable) electricity between residential agents (consumers, prosumers, and producers) within their community. Based on a structured literature review, a market engineering framework for LEMs is developed. The work focuses on two of the framework\u27s eight components, namely the agent behavior and the (micro) market structure. Residential agent behavior is evaluated in two steps. Firstly, two empirical studies, a structural equation model-based survey with 195 respondents and an adaptive choice-based conjoint study with 656 respondents, are developed, conducted and evaluated. Secondly, a discount price LEM is designed following the surveys\u27 results. Theoretical solutions of the LEM bi-level optimization problem with complete information and heuristic reinforcement learning with incomplete information are investigated in a multi-agent simulation to find the profit-maximizing market allocations. The (micro) market structure is investigated with regards to LEM business models, information systems and real-world application projects. Potential business models and their characteristics are combined in a taxonomy based on the results of 14 expert interviews. Then, the Smart Grid Architecture Model is utilized to derive the organizational, informational, and technical requirements for centralized and distributed information systems in LEMs. After providing an overview on current LEM implementations projects in Germany, the Landau Microgrid Project is used as an example to test the derived requirements. In conclusion, the work recommends current LEM projects to focus on overall discount electricity trading. Premium priced local electricity should be offered to subgroups of households with individual higher valuations for local generation. Automated self-learning algorithms are needed to mitigate the trading effort for residential LEM agents in order to ensure participation. The utilization of regulatory niches is suggested until specific regulations for LEMs are established. Further, the development of specific business models for LEMs should become a prospective (research) focus

日射量予測を考慮した太陽光発電コミュニティにおけるエネルギーシェアリングに関する研究

The power sector plays an important role in energy conservation and emission reduction. Renewable energy, especially solar PV, has been growing steadily in recent years. The development of solar energy can not only reduce the use of fossil energy, but also increase the energy self-sufficiency rate. After the implementation of the FiT system in 2011, there has been an explosive growth in the import of solar PV. However, solar power generation exhibits unstable output characteristics as it is affected by weather conditions. Large-scale introduction can affect the stability of the grid. Therefore, this study considers the unstable weather conditions (mainly, solar radiation) and proposes the concept of energy sharing to increase the chances of local energy self-consumption and renewable energy penetration in the future. At the same time, we aim to explore the interactions between smart grids, smart buildings, and distributed energy storage to achieve better energy management practices.北九州市立大

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios