A Study on the Hierarchical Control Structure of the Islanded Microgrid

The microgrid is essential in promoting the power system’s resilience through its ability to host small-scale DG units. Furthermore, the microgrid can isolate itself during main grid faults and supply its demands. However, islanded operation of the microgrid is challenging due to difficulties in frequency and voltage control. In islanded mode, grid-forming units collaborate to control the frequency and voltage. A hierarchical control structure employing the droop control technique provides these control objectives in three consecutive levels: primary, secondary, and tertiary. However, challenges associated with DG units in the vicinity of distribution networks limit the effectiveness of the islanded mode of operation.In MV and LV distribution networks, the X/R ratio is low; hence, the frequency and voltage are related to the active and reactive power by line parameters. Therefore, frequency and voltage must be tuned for changes in active or reactive powers. Furthermore, the line parameters mismatch causes the voltage to be measured differently at each bus due to the different voltage drops in the lines. Hence, a trade-off between voltage regulation and reactive power-sharing is formed, which causes either circulating currents for voltage mismatch or overloading for reactive power mismatch. Finally, the economic dispatch is usually implemented in tertiary control, which takes minutes to hours. Therefore, an estimation algorithm is required for load and renewable energy quantities forecasting. Hence, prediction errors may occur that affect the stability and optimality of the control. This dissertation aims to improve the power system resilience by enhancing the operation of the islanded microgrid by addressing the above-mentioned issues. Firstly, a linear relationship described by line parameters is used in droop control at the primary control level to accurately control the frequency and voltage based on measured active and reactive power. Secondly, an optimization-based consensus secondary control is presented to manage the trade-off between voltage regulation and reactive power-sharing in the inductive grid with high line parameters mismatch. Thirdly, the economic dispatch-based secondary controller is implemented in secondary control to avoid prediction errors by depending on the measured active and reactive powers rather than the load and renewable energy generation estimation. The developed methods effectively resolve the frequency and voltage control issues in MATLAB/SIMULINK simulations

Realizing the potential of distributed energy resources and peer-to-peer trading through consensus-based coordination and cooperative game theory

Driven by environmental and energy security concerns, a large number of small-scale distributed energy resources (DERs) are increasingly being connected to the distribution network. This helps to support a cost-effective transition to a lower-carbon energy system, however, brings coordination challenges caused by variability and uncertainty of renewable energy resources (RES). In this setting, local flexible demand (FD) and energy storage (ES) technologies have attracted great interests due to their potential flexibility in mitigating the generation and demand variability and improving the cost efficiency of low-carbon electricity systems. The combined effect of deregulation and digitalization inspired new ways of exchanging electricity and providing management/services on the paradigm of peer-to-peer (P2P) and transparent transactions. P2P energy trading enables direct energy trading between prosumers, which incentivizes active participation of prosumer in the trading of electricity in the distribution network, in the meantime, the efficient usage of FD and ES owned by the prosumers also facilitates better local power and energy balance. Though the promising P2P energy trading brings numerous advancements, the existing P2P mechanisms either fail to coordinate energy in a fully distributed way or are unable to adequately incentivize prosumers to participate, preventing prosumers from accessing the highest achievable monetary benefits and/or suffering severely from the curse of dimensionality. Therefore, this thesis aims at proposing three P2P energy trading enabling mechanisms in the aspect of fully distributed efficient balanced energy coordination through consensus-based algorithm and two incentivizing pricing and benefit distribution mechanisms through cooperative game theory. Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of DER due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of FD and ES resources. This thesis demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. Moreover, this thesis proposes two computationally efficient pricing and benefit distribution mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core tatonnement process while the second involves a novel pricing mechanism based on the solution of single linear programming. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition.Open Acces

HVAC-based hierarchical energy management system for microgrids

With the high penetration of renewable energy into the grid, power fluctuations and supply-demand power mismatch are becoming more prominent, which pose a great challenge for the power system to eliminate negative effects through demand side management (DSM). The flexible load, such as heating, ventilation, air conditioning (HVAC) system, has a great potential to provide demand response services in the electricity grids. In this thesis, a comprehensive framework based on a forecasting-management optimization approach is proposed to coordinate multiple HVAC systems to deal with uncertainties from renewable energy resources and maximize the energy efficiency. In the forecasting stage, a hybrid model based on Multiple Aggregation Prediction Algorithm with exogenous variables (MAPAx)-Principal Components Analysis (PCA) is proposed to predict changes of local solar radiance, vy using the local observation dataset and real-time meteorological indexes acquired from the weather forecast spot. The forecast result is then compared with the statistical benchmark models and assessed by performance evaluation indexes. In the management stage, a novel distributed algorithm is developed to coordinate power consumption of HVAC systems by varying the compressors’ frequency to maintain the supply-demand balance. It demonstrates that the cost and capacity of energy storage systems can be curtailed, since HVACs can absorb excessive power generation. More importantly, the method addresses a consensus problem under a switching communication topology by using Lyapunov argument, which relaxes the communication requirement. In the optimization stage, a price-comfort optimization model regarding HVAC’s end users is formulated and a proportional-integral-derivative (PID)-based distributed algorithm is thus developed to minimize the customer’s total cost, whilst alleviating the global power imbalance. The end users are motivated to participate in energy trade through DSM scheme. Furthermore, the coordination scheme can be extended to accommodate battery energy storage systems (BESSs) and a hybrid BESS-HVAC system with increasing storage capacity is proved as a promising solution to enhance its selfregulation ability in a microgrid. Extensive case studies have been undertaken with the respective control strategies to investigate effectiveness of the algorithms under various scenarios. The techniques developed in this thesis has helped the partnership company of this project to develop their smart immersion heaters for the customers with minimum energy cost and maximum photovoltaic efficiency

Distributed Market Clearing Approach for Local Energy Trading in Transactive Market

This paper proposes a market clearing mechanism for energy trading in a local transactive market, where each player can participate in the market as seller or buyer and tries to maximize its welfare individually. Market players send their demand and supply to a local data center, where clearing price is determined to balance demand and supply. The topology of the grid and associated network constraints are considered to compute a price signal in the data center to keep the system secure by applying this signal to the corresponding players. The proposed approach needs only the demanded/supplied power by each player to reach global optimum which means that utility and cost function parameters would remain private. Also, this approach uses distributed method by applying local market clearing price as coordination information and direct load flow (DLF) for power flow calculation saving computation resources and making it suitable for online and automatic operation for a market with a large number of players. The proposed method is tested on a market with 50 players and simulation results show that the convergence is guaranteed and the proposed distributed method can reach the same result as conventional centralized approach.Comment: Accepted paper. To appear in PESGM 2018, Portland, OR, 201

Distributed cooperative control for economic operation of multiple plug‐in electric vehicle parking decks

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138231/1/etep2348.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138231/2/etep2348_am.pd

Distributed Optimal Frequency Control Considering a Nonlinear Network-Preserving Model

This paper addresses the distributed optimal frequency control of power systems considering a network-preserving model with nonlinear power flows and excitation voltage dynamics. Salient features of the proposed distributed control strategy are fourfold: i) nonlinearity is considered to cope with large disturbances; ii) only a part of generators are controllable; iii) no load measurement is required; iv) communication connectivity is required only for the controllable generators. To this end, benefiting from the concept of 'virtual load demand', we first design the distributed controller for the controllable generators by leveraging the primal-dual decomposition technique. We then propose a method to estimate the virtual load demand of each controllable generator based on local frequencies. We derive incremental passivity conditions for the uncontrollable generators. Finally, we prove that the closed-loop system is asymptotically stable and its equilibrium attains the optimal solution to the associated economic dispatch problem. Simulations, including small and large-disturbance scenarios, are carried on the New England system, demonstrating the effectiveness of our design

Distributed Voltage Control in Distribution Networks with Electric Vehicle Charging Stations and Photovoltaic Generators

The developments of distributed generators (DGs) and electric vehicles (EVs) are dramatical due to the rapid increase of friendly environment desire. While on another hand, the proliferation of distributed generators (DGs) and electric vehicle charging stations (EVCSs) has brought voltage regulation challenges to distribution systems due to their high generations and heavy loads. In this thesis, a distributed control strategy is proposed which mainly consisted by a reactive compensation algorithm to dispatch surplus reactive power from DGs and EVCSs for proper voltage regulation without violating their converters’ capacity limits or stressing conventional voltage control devices, i.e., on-load tap changers (OLTCs), and an active power curtailment algorithm for DGs to properly integrate OLTC in voltage regulation when the reactive power compensation is deficient. The proposed control algorithms rely on consensus theory and sensitivity analysis, thus, minimizing the active and reactive powers needed for voltage support, and decreasing the net cost of voltage regulation. In the proposed control strategy, three distributed voltage regulation algorithms, as well as a distributed control method for OLTC, are developed and coordinated to realize adequate voltage maintaining effects. Simulation results of a typical distribution system confirm the effectiveness and robustness of the proposed distributed control strategy in continuously maintaining proper voltage regulation for the whole distribution system with minimum power demands from DGs and EVCSs, and reduced tap operation for OLTC, within every 24 hours