Robust optimization for energy transactions in multi-microgrids under uncertainty

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid

An electricity market with fast bidding, planning and balancing in smart grids

In future energy systems, peaks in the daily electricity generation and consumption are expected to increase. The "smart grid" concept aims to maintain high levels of efficiency in the energy system by establishing distributed intelligence. Software agents (operating on devices with unknown computational capabilities) can implement dynamic and autonomous decision making about energy usage and generation, e.g. in domestic households, farms or offices. To reach satisfactory levels of efficiency and reliability, it is crucial to include planning-ahead of the energy-involving activities. Market mechanisms are a promising approach for large-scale coordination problems about energy supply and demand, but existing electricity markets either do not involve planning-ahead sufficiently or require a high level of sophistication and computing power from participants, which is not suitable for smart grid settings. This paper proposes a new market mechanism for smart grids, ABEM (Ahead- and Balancing Energy Market). ABEM performs an ahead market and a last-minute balancing market, where planning-ahead in the ahead market supports both binding ahead-commitments and reserve capacities in bids (which can be submitted as price functions). These features of planning-ahead reflect the features in modern wholesale electricity markets. However, constructing bids in ABEM is straightforward and fast. We also provide a model of a market with the features mentioned above, which a strategic agent can use to construct a bid (e.g. in ABEM), using a decision-theoretic approach. We evaluate ABEM experimentally in various stochastic scenarios and show favourable outcomes in comparison with a benchmark mechanism

Allocation of Resources for Protecting Public Goods against Uncertain Threats Generated by Agents

This paper analyses a framework for designing robust decisions against uncertain threats to public goods generated by multiple agents. The agents can be intentional attackers such as terrorists, agents accumulating values in flood or earthquake prone locations, or agents generating extreme events such as electricity outage and recent BP oil spill, etc. Instead of using a leader-follower game theoretic framework, this paper proposes a decision theoretic model based on two-stage stochastic optimization (STO) models for advising optimal resource allocations (or regulations) in situations characterized by uncertain perceptions of agent behaviors. In particular, the stochastic mini-max model and multi- shortfalls) is advanced in the context of quantile optimization for dealing with potential extreme events. Proposed framework can deal with both direct and indirect judgments on the decision makers perception about uncertain agent behaviors, either directly by probability density estimation, or indirectly by probabilistic inversion. The quantified distributions are treated as input to the stochastic optimization models in order to address inherent uncertainties. Robust decisions can then be obtained against all possible threats, especially with extreme consequences. This paper also introduces and compares three different computational algorithms which can be used to solve arising two-stage STO problems, including bilateral descent method, linear programming approximation and stochastic quasi-gradient method. A numerical example of high dimensionlity is presented for illustration of their performance under large number of scenarios typically required for dealing with low probability extreme events. Case studies include deensive resource allocations among cities and security of electricity networks

Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

Application of Agent-Based Modeling to Complex Systems

This dissertation examines the application of agent-based modeling (ABM) to complex systems with the intent of developing a means of overcoming limitations present in existing tools. This is done though the development of two ABMs intended to address complex systems present in the fields of sustainability studies and chemistry. After introductory information, Chapters 2 - 4 of this dissertation address the limitations of tools intended to project the environmental, economic, and social impacts of woody biomass based biofuels. Chapter 2 begins by discussing the limitations in tools to study timber harvest decision making and its impact upon the landscape, and develops an ABM platform to address this gap. Next, Chapter 3 presents a life cycle assessment (LCA) of a proposed biorefinery in Ontonagon, Michigan is conducted. This study acts as a benchmark benchmark for the case study presented in Chapter 4, where an argument for the integration of ABM and life cycle sustainability assessment (agent-based LCSA) is presented. The argument is followed by a case study demonstrating the applicability of the technique. The case study finds that while Ontonagon is a promising site for a biorefinery, there are concerns regarding the quantity of woody biomass that may be delivered as a feedstock and potential impacts upon regional wetlands. Chapter 5 of this dissertation addresses the limitations of models of advanced oxidation processes (AOPs) using ordinary differential equations (ODEs). We argue that these limitations can be addressed by modeling the AOP as a complex system, including the complete elementary reaction pathway using ABM. To demonstrate the applicability of this novel approach, an ABM is developed and two in silico studies of acetone degradation induced by hydroxyl radicals are performed. We found that when using a comprehensive list of elementary reaction pathways, the ABM was able to replicate concentration curves for major chemical species in our laboratory study. As a novel application of ABM to AOPs we conclude that the technique shows considerable promise

Unit Commitment Problem in Electrical Power System: A Literature Review

Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties

Multi-agent system based active distribution networks

This thesis gives a particular vision of the future power delivery system with its main requirements. An investigation of suitable concepts and technologies which creates a road map forward the smart grid has been carried out. They should meet the requirements on sustainability, efficiency, flexibility and intelligence. The so called Active Distribution Network (ADN) is introduced as an important element of the future power delivery system. With an open architecture, the ADN is designed to integrate various types of networks, i.e., MicroGrid or Autonomous Network, and different forms of operation, i.e., islanding or interconnection. By enabling an additional local control layer, these so called cells are able to reconfigure, manage local faults, support voltage regulation, or manage power flow. Furthermore, the Multi-Agent System (MAS) concept is regarded as a potential technology to cope with the anticipated challenges of future grid operation. Analysis of benefits and challenges of implementing MAS shows that it is a suitable technology for a complex and highly dynamic operation and open architecture as the ADN. By taking advantages of the MAS technology, the AND is expected to fully enable distributed monitoring and control functions. This MAS-based ADN focuses mainly on control strategies and communication topologies for the distribution systems. The transition to the proposed concept does not require an intensive physical change to the existing infrastructure. The main point is that inside the MAS-based ADN, loads and generators interact with each other and the outside world. This infrastructure can be built up of several cells (local areas) that are able to operate autonomously by an additional agent-based control layer. The ADN adapts a MAS hierarchical control structure in which each agent handles three functional layers of management, coordination, and execution. In the operational structure, the ADN addresses two main function parts: Distributed State Estimation (DSE) to analyze the network topology, compute the state estimation, and detect bad data; and Local Control Scheduling (LCS) to establish the control set points for voltage coordination and power flow management. Under the distributed context of the controls, an appropriate method for DSE is proposed. The method takes advantage of the MAS technology to compute iteratively the local state variables through neighbor data measurements. Although using the classical Weighted Least Square (WLS) as a core, the proposed algorithm based on an agent environment distributes drastically computation burden to subtasks of state estimation with only two interactive buses and an interconnection line in between. The accuracy and complexity of the proposed estimation are investigated through both off-line and on-line simulations. Distributed and parallel working of processors improves significantly the computation time. This estimation is also suitable for a meshed configuration of the ADN, which includes more than one interconnection between each pair of the cells. Depending on the availability of a communication infrastructure, it is able to work locally inside the cells or globally for the whole ADN. As a part of the LCS, the voltage control function is investigated in both steady-state and dynamic environments. The autonomous voltage control within each network area (cell) can be deployed by a combination of active and reactive power support of distributed generation (DG). The coordinated voltage control defines the optimal tap setting of the on-load tap changer (OLTC) while comparing amounts of control actions in each area. Based on the sensitivity factors, these negotiations are thoroughly supported in the distributed environment of the MAS platform. To verify the proposed method, both steady-state and dynamic simulations are developed. Simulation results show that the proposed function helps to integrate more DG while mitigating voltage violation effectively. The optimal solution can be reached within a small number of calculation iterations. It opens a possibility to apply the proposed method as an on-line application. Furthermore, a distributed approach for the power flow management function is developed. By converting the power network to a represented graph, the optimal power flow is understood as the well-known minimum cost flow problem. Two fundamental solutions for the minimum cost flow, i.e., the Successive Shortest Path (SSP) algorithm and the Cost-Scaling Push-Relabel (CS-PR) algorithm, are introduced. The SSP algorithm is augmenting the power flow along the shortest path until reaching the capacity of at least one edge. After updating the flow, it finds another shortest path and augments the flow again. The CS-PR algorithm approaches the problem in a different way which is scaling cost and pushing as much flow as possible at each active node. Simulations of both meshed and radial test networks are developed to compare their performances in various network conditions. Simulation results show that the two methods can allow both generation and power flow controller devices to operate optimally. In the radial test network, the CS-PR needs less computation effort represented by a number of exchanged messages among the MAS platform than the SSP. Their performances in the meshed network are, however, almost the same. Last but not least, this novel concept of MAS-based AND is verified under a laboratory environment. The lab set-up separates some local network areas by using a three-inverter system. The MAS platform is created on different computers and is able to retrieve data from and to hardware components, i.e., the three-inverter system. In this set-up, a configuration of the power router is established in a combination of the three-inverter system with the MAS platform. Three control functions of the inverters, AC voltage control, DC bus voltage control, and PQ control, are developed in a Simulink diagram. By assigning suitable operation modes for the inverters, the set-up successfully experiments on synchronizing and disconnecting a cell to the rest of the grid. In the MAS platform, an obvious power routing strategy is executed to optimally manage power flow in the lab set-up. The results show that the proposed concept of the ADN with the power router interface works well and can be used to manage electrical networks with distributed generation and controllable loads, leading to active networks