Bottom-up self-organization of unpredictable demand and supply under decentralized power management

In the DEZENT1 project we had established a distributed base model for negotiating electric power from widely distributed (renewable) power sources on multiple levels in succession. Negotiation strategies would be intelligently adjusted by the agents, through (distributed) Reinforcement Learning procedures. The distribution of the negotiated power quantities (under distributed control as well) occurs such that the grid stability is guaranteed, under 0.5 sec. The major objective in this paper was to deal, on the same level of granularity, with short-term power balance fluctuation, in terms of a peak demand and supply management exhibiting highly dynamic, self-organizing, autonomous yet coordinated algorithms under fine-grained distributed control. Our extensive experiments show very clearly that these short-term fluctuations could be leveled down by 70 - 75 %. In this way we have tackled, for the quickly increasing renewable power systems, a crucial problem of its stability, in a novel way that scales very easily due to the completely decentralized control

Networked and Distributed Control Method with Optimal Power Dispatch for Islanded Microgrids

In this paper, a two-layer network and distributed control method is proposed, where there is a top-layer communication network over a bottom-layer microgrid. The communication network consists of two subgraphs, in which the first is composed of all agents, while the second is only composed of controllable agents. The distributed control laws derived from the first subgraph guarantee the supply-demand balance, while further control laws from the second subgraph reassign the outputs of controllable distributed generators, which ensure active and reactive power are dispatched optimally. However, for reducing the number of edges in the second subgraph, generally a simpler graph instead of a fully connected graph is adopted. In this case, a near-optimal dispatch of active and reactive power can be obtained gradually, only if controllable agents on the second subgraph calculate set points iteratively according to our proposition. Finally, the method is evaluated over seven cases via simulation. The results show that the system performs as desired, even if environmental conditions and load demand fluctuate significantly. In summary, the method can rapidly respond to fluctuations resulting in optimal power sharing

Autonomous energy management system with self-healing capabilities for green buildings (microgrids)

Nowadays, distributed energy resources are widely used to supply demand in micro grids specially in green buildings. These resources are usually connected by using power electronic converters, which act as actuators, to the system and make it possible to inject desired active and reactive power, as determined by smart controllers. The overall performance of a converter in such system depends on the stability and robustness of the control techniques. This paper presents a smart control and energy management of a DC microgrid that split the demand among several generators. In this research, an energy management system ( EMS) based on multi-agent system ( MAS) controllers is developed to manage energy, control the voltage and create balance between supply and demand in the system with the aim of supporting the reliability characteristic. In the proposed approach, a reconfigurated hierarchical algorithm is implemented to control interaction of agents, where a CAN bus is used to provide communication among them. This framework has ability to control system, even if a failure appears into decision unit. Theoretical analysis and simulation results for a practical model demonstrate that the proposed technique provides a robust and stable control of a microgrid

Management of Distributed Energy Storage Systems for Provisioning of Power Network Services

Because of environmentally friendly reasons and advanced technological development, a significant number of renewable energy sources (RESs) have been integrated into existing power networks. The increase in penetration and the uneven allocation of the RESs and load demands can lead to power quality issues and system instability in the power networks. Moreover, high penetration of the RESs can also cause low inertia due to a lack of rotational machines, leading to frequency instability. Consequently, the resilience, stability, and power quality of the power networks become exacerbated. This thesis proposes and develops new strategies for energy storage (ES) systems distributed in power networks for compensating for unbalanced active powers and supply-demand mismatches and improving power quality while taking the constraints of the ES into consideration. The thesis is mainly divided into two parts. In the first part, unbalanced active powers and supply-demand mismatch, caused by uneven allocation and distribution of rooftop PV units and load demands, are compensated by employing the distributed ES systems using novel frameworks based on distributed control systems and deep reinforcement learning approaches. There have been limited studies using distributed battery ES systems to mitigate the unbalanced active powers in three-phase four-wire and grounded power networks. Distributed control strategies are proposed to compensate for the unbalanced conditions. To group households in the same phase into the same cluster, algorithms based on feature states and labelled phase data are applied. Within each cluster, distributed dynamic active power balancing strategies are developed to control phase active powers to be close to the reference average phase power. Thus, phase active powers become balanced. To alleviate the supply-demand mismatch caused by high PV generation, a distributed active power control system is developed. The strategy consists of supply-demand mismatch and battery SoC balancing. Control parameters are designed by considering Hurwitz matrices and Lyapunov theory. The distributed ES systems can minimise the total mismatch of power generation and consumption so that reverse power flowing back to the main is decreased. Thus, voltage rise and voltage fluctuation are reduced. Furthermore, as a model-free approach, new frameworks based on Markov decision processes and Markov games are developed to compensate for unbalanced active powers. The frameworks require only proper design of states, action and reward functions, training, and testing with real data of PV generations and load demands. Dynamic models and control parameter designs are no longer required. The developed frameworks are then solved using the DDPG and MADDPG algorithms. In the second part, the distributed ES systems are employed to improve frequency, inertia, voltage, and active power allocation in both islanded AC and DC microgrids by novel decentralized control strategies. In an islanded DC datacentre microgrid, a novel decentralized control of heterogeneous ES systems is proposed. High- and low frequency components of datacentre loads are shared by ultracapacitors and batteries using virtual capacitive and virtual resistance droop controllers, respectively. A decentralized SoC balancing control is proposed to balance battery SoCs to a common value. The stability model ensures the ES devices operate within predefined limits. In an isolated AC microgrid, decentralized frequency control of distributed battery ES systems is proposed. The strategy includes adaptive frequency droop control based on current battery SoCs, virtual inertia control to improve frequency nadir and frequency restoration control to restore system frequency to its nominal value without being dependent on communication infrastructure. A small-signal model of the proposed strategy is developed for calculating control parameters. The proposed strategies in this thesis are verified using MATLAB/Simulink with Reinforcement Learning and Deep Learning Toolboxes and RTDS Technologies' real-time digital simulator with accurate power networks, switching levels of power electronic converters, and a nonlinear battery model

Performance of industrial melting pots in the provision of dynamic frequency response in the Great Britain power system

As a result of the increasing integration of Renewable Energy Source (RES), maintenance of the balance between supply and demand in the power system is more challenging because of RES’s intermittency and uncontrollability. The smart control of demand is able to contribute to the balance by providing the grid frequency response. This paper uses the industrial Melting Pot (MP) loads as an example. A thermodynamic model depicting the physical characteristics of MPs was firstly developed based on field measurements carried out by Open Energi. A distributed control was applied to each MP which dynamically changes the aggregated power consumption of MPs in proportion to changes in grid frequency while maintaining the primary heating function of each MP. An aggregation of individual MP models equipped with the control was integrated with the Great Britain (GB) power system models. Case studies verified that the aggregated MPs are able to provide frequency response to the power system. The response from MPs is similar but faster than the conventional generators and therefore contributes to the reduction of carbon emissions by replacing the spinning reserve capacity of fossil-fuel generators. Through the reviews of the present balancing services in the GB power system, with the proposed frequency control strategy, the Firm Frequency Response service is most beneficial at present for demand aggregators to tender for. All studies have been conducted in partnership between Cardiff University, Open Energi London – Demand Aggregator, and National Grid – System Operator in GB to ensure the quality and compliance of results

Power loss minimisation of off-grid solar DC nano-grids - part II : a quasi-consensus-based distributed control algorithm

This paper investigates the power loss minimization problem of solar DC nanogrids that are designed to provide energy access to households in off-grid areas. We consider nano-grids with distributed battery storage energy systems and that are enabled by multi-port DC-DC converters. As the nano-grids are not connected to the national grid and have batteries and converters distributed in each household, addressing the power loss problem while ensuring supply-demand balance is a challenge. To address the challenge, we propose a novel quasi-consensus based distributed control approach. The proposed approach consists of two algorithms namely, incremental loss consensus algorithm and voltage consensus algorithm. The incremental loss consensus algorithm is proposed to optimally schedule the battery charge/discharge operation while ensuring that supply-demand balance and the battery constraints are satisfied. The voltage consensus algorithm is proposed to determine optimal distribution voltage set points which act as optimal control signals. Both algorithms are implemented in a distributed manner, where minimal information exchange between households is required to obtain the optimal control actions. Simulation results of a solar DC nano-grid with five interconnected households verify the effectiveness of the proposed approach at addressing the nano-grid power loss problem

Single core configurations of saturated core fault current limiter performance of laboratory test models

Economic growth with industrialization and urbanization lead to an extensive increase in power demand. It forced the utilities to add power generating facilities to cause the necessary demand-generation balance. The bulk power generating stations, mostly interconnected, with the penetration of distributed generation result in an enormous rise in the fault level of power networks. It necessitates for electrical utilities to control the fault current so that the existing switchgear can continue its services without up-gradation or replacement for reliable supply. The deployment of fault current limiter (FCL) at the distribution and transmission networks has been under investigation as a potential solution to the problem. A saturated core fault current limiter (SCFCL) technology is a smart, scalable, efficient, reliable, and commercially viable option to manage fault levels in existing and future MV/HV supply systems. This paper presents the comparative performance analysis of two single-core SCFCL topologies impressed with different core saturations. It has demonstrated that the single AC winding configuration needs more bias power for affecting the same current limiting performance with an acceptable steady-state voltage drop contribution. The fault state impedance has a transient nature, and the optimum bias selection is a critical design parameter in realizing the SCFCL applications