Game theoretic optimisation in process and energy systems engineering: A review

Game theory is a framework that has been used by various research fields in order to represent dynamic correlation among stakeholders. Traditionally, research within the process and energy systems engineering community has focused on the development of centralised decision making schemes. In the recent years, decentralised decision-making schemes have attracted increasing attention due to their ability to capture multi-stakeholder dynamics in a more accurate manner. In this article, we survey how centralised and decentralised decision making has been facilitated by game theoretic approaches. We focus on the deployment of such methods in process systems engineering problems and review applications related to supply chain optimisation problems, design and operations, and energy systems optimisation. Finally, we analyse different game structures based on the degree of cooperation and how fairness criteria can be employed to find fair payoff allocations

Multi-objective Smart Charge Control of Electric Vehicles

With the increasing integration of electric vehicles and renewable energy sources in electricity networks, key opportunities in terms of a cleaner environment and a sustainable energy portfolio are unlocked. However, the widespread deployment of these two technologies, can entail significant challenges for the electricity grid and in a larger context for the society, when they are not optimally integrated. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are being proposed as crucial solutions to achieve economic, technical and environmental benefits in future smart grids. The implementation of these technologies involves a number of key stakeholders, namely, the end-electricity user, the electric vehicle owner, the system operators and policy makers. For a wider and efficient implementation of the smart grid vision, these stakeholders must be engaged and their aims must be fulfilled. However, the financial, technical and environmental objectives of these stakeholders are often conflicting, which leads to an intricate paradigm requiring efficient and fair policies. With this focus in mind, the present research work develops multi-objective optimisation algorithms to control the charging and discharging process of electric vehicles. Decentralised, hybrid and real-time optimisation algorithms are proposed, modelled, simulated and validated. End user energy cost, battery degradation, grid interaction and CO2 emissions are optimised in this work and their trade-offs are highlighted. Multi-criteria-decision-making approaches and game theoretical frameworks are developed to conciliate the interests of the involved stakeholders. The results, in the form of optimal electric vehicle charging/discharging schedules, show improvements along all the objectives while complying with the user requirements. The outcome of the present research work serves as a benchmark for informing system operators and policy makers on the necessary measures to ensure an efficient and sustainable implementation of electro-mobility as a fundamental part of current and future smart grids

Planning and Operation Framework of Smart Distributed Energy Resources in Emerging Distribution Systems

Smart grid technologies provoke a major paradigm shift in how power systems are planned and operated. The transition to a smarter system is happening gradually; however, researchers has reported that this transition is dealt with generally in a "react and provide" manner. Proper planning studies allow for this transition to be done in a predict and provide" fashion. Part of planning this transition is envisioning the future system. Technical, social, environmental, and economical challenges are foreseen and tackled in the literature; however, no generalized planning framework that addresses the overall picture of different involved parties' interests and their anticipated (and often conflicting) interactions to properly plan for a better and fair outcome has been developed. This work addresses the issue in several steps: 1) it provides a backbone framework architecture for asset sizing and allocation in the future Smart Distribution System (SDS), 2) it considers the daily optimal operation of these assets in the long-term planning problem, and 3) it considers the potential conflicts that happen on the long-term planning level and operational levels. This architecture requires the development of a framework capable of absorbing private investments, integrating new technologies, promoting smart grid applications, and yet remaining feasible to all involved parties. Strategic analysis of the involvement of each stakeholder has been conducted. Proceeding from this analysis, deductions and conclusions about venues for promoting and allowing a smoother transition to the new paradigm are drawn. In addition, this analysis highlighted potential conflicts that are showcased in two different case studies. Potential ways in which the conditions affect the planning procedure and how they can be overcome are proposed. The recommendations can be highlighted as follows: 1) promoting new smart grid technologies, 2) encouraging communications and cooperation between involved parties, 3) considering daily optimal operation of assets to fully take advantage of their new active nature to better allocate them in the long-term planning problem, and 4) consideration of stakeholders interests in the planning phase in order to better absorb investments and move to the new paradigm. To size and allocate assets in the long-term planning problem for the SDS, first a building algorithm has been developed to size and allocate DG units. This algorithm breaks the problem into two subproblems to overcome the modeling and computational challenges of the mixed-integer nonlinear programming problem. The first subproblem is addressed using heuristic optimization techniques, namely a genetic algorithm, and the second involving deterministic analytical means of nonlinear optimization, utilizing the advancements made in branch-and-bound methods providing a proven global optimal solution to non-convex problems. Considering the daily optimal operation and both electric utilities' and investors' objectives, the planning problem has been developed. The results show greater private investments absorption, reduced costs to both parties, and higher system performance due to lowered energy losses. The expected increased numbers of customers opting to become resilient and have a more reliable service pose several operational and planning challenges. In this work, a novel consensus-based algorithm is introduced as an economically efficient tool for coordinating prosumers' interactions, within the feasible solution region. Several objectives are targeted in this work, among which the global economic benefit maximization of all interacting prosumers is the most salient. This economic benefit comprises the total cooperative payoff of the interacting prosumers. Each prosumer has its own private bounds defining the range of power production and consumption. A novel definition is proposed for prosumers' interaction in the hybrid microgrids. The developed scheme's importance stems from the dramatic change in the smart networks' paradigms. In addition, the individual prosumers' preferences are recognized via the comprehensive mathematical modeling for the evolved AC/DC network. The results are provided for a basic two-prosumer scenario. However, these results highlight the potential of the proposed approach in a practical system setting. More sophisticated case studies, i.e., multi-power levels, multi-prosumers, and different system topologies, could be also studied using the proposed work

Energy Sharing Models for Renewable Energy Integration: Subtransmission Level, Distribution Level, and Community Level

Distributed energy resources (DERs) are being embedded rapidly and widely in the power grid and promoting the transformation of the centralized power industry to a more deregulated mode. However, how to safely and efficiently consume renewable energy is becoming a major concern. In this regard, energy sharing at both grid-scale and community-scale has emerged as a new solution to encourage participants to actively bid instead of acting as price takers and has the potential to accelerate the integration of DERs and decrease energy costs. At the grid level, two risk-averse energy sharing models are developed to safely integrate renewable energy by considering the network constraints and overbidding risk. A risk-averse two-stage stochastic game model is proposed for the regional energy sharing market (ESM). The sample average approximation (SAA) method is used to approximate the stochastic Cournot-Nash equilibrium. In addition, a data-driven joint chance-constrained game is developed for energy sharing in the local energy market (LEM). This model considers the maximum outputs of renewable energy aggregators (REAs) are random variables whose probability distributions are unknown, but the decision-maker has access to finite samples. Case studies show that the proposed game models can effectively increase the profit of reliable players and decrease the overbidding risk. At the community level, a community server enables energy sharing among users based on the Bayesian game-based pricing mechanism. It can also control the community energy storage system (CESS) to smooth the load based on the grid's price signal. A communication-censored ADMM for sharing problems is developed to decrease the communication cost between the community and the grid. Moreover, a co-optimization model for the plan and operation of the shared CESS is developed. By introducing the price uncertainty and degradation cost, the proposed model could more accurately evaluate the performance of the CESS and tap more economic potential. This thesis provides proof of the Nash equilibrium of all game models and the convergence of all market clearing algorithms. The proposed models and methods present performance improvement compared with existing solutions. The work in this thesis indicates that energy sharing is possible to implement at different levels of the power system and could benefit the participants and promote the integration of DERs

Towards Smarter Electric Vehicle Charging with Low Carbon Smart Grids: Pricing and Control.

Environmental and political directions indicate transition to a decarbonized transportation system is necessary as it is one of the most pollutant sectors regarding greenhouse gas emissions. Research in Demand Side Management suggests that its tools are the most cost-effective option for improving the performance of the grid without incurring into high infrastructure investments, hence reducing the payback for start-ups in the sector. This Thesis proposes solutions to tackle 5 objectives around this area of research: 1-2 are related to developing a demand response pricing and EV smart charging strategies, 3-4 are related to developing a multi-objective charging scheme in order to ensure fairness and reduction of CO2eq emissions, and 5 is related to testing parameters of EV charging to understand future improvements and limitations in the proposed models. Chapter 3, that tackles objectives 1-2, proposes a data-driven optimisation algorithm with pricing and control modules that communicate with each other to achieve a successful integration with the grid by charging at the right price and expected time. The results show customers can be positively engaged with pricing signals while providing support to the grid. Chapter 4, which tackles objectives 3-4, proposes a multi-objective EV charging formulation that include perspectives of EV users, a carbon regulator and a charging station operator. The multi-objective formulation is solved with a genetic algorithm in order to find the fairest and the greenest solution. Results which are evaluated using different scenarios show different weights to each objective function can differ based on the charging location and EV charging availability. Finally, Chapter 5 which tackles objective 5, shows a sensitivity analysis where improvements in revenues, reduction of carbon emissions and bidding capacity depend on the evaluation of EV users’ parameters, and the charging station control and sizing

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Public-Private Collaborations in Emergency Logistics: A Framework based on Logistical and Game-Theoretical Concepts

Collaboration in emergency logistics can be beneficial for governmental actors when supply chains need to be set up immediately. In comparison to research on humanitarian-business partnerships, the body of literature on so-called Public-Private Emergency Collaborations (PPEC) remains scarce. Private companies are only rarely considered within research on emergency collaborations, although they could contribute to a more efficient supply of goods given their resources and existing communication networks. Based on this research gap, this paper develops a logistical and game-theoretical modeling framework for public-private emergency collaborations. We characterize both public and private actors\u27 possible roles in emergency logistics based on literature research and real cases. Furthermore, we provide an overview on existing PPECs and the challenges they are confronted with. The concluding framework contains aspects from humanitarian logistics on the governmental side and from business continuity management (BCM) or corporate social responsibility (CSR) on the commercial side. To address the challenge of evaluating different objectives in a collaboration, we add a game-theoretical approach to highlight the incentive structure of both parties in such a collaboration. In this way, we contribute to the research field by quantitatively evaluating public-private collaboration in emergency logistics while considering the problem-specific challenge of the parties\u27 different objectives