Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/1/etep12366.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/2/etep12366_am.pd

Optimal Control of Power Quality in Microgrids Using Particle Swarm Optimisation

Driven by environmental protection, economic factors, conservation of energy resources, and technical challenges, the microgrid has emerged as an innovative small-scale power generation network. Microgrids consist of a cluster of Distributed Generation units that encompass a portion of an electric power distribution system and may rely on different energy sources. Functionally, the microgrid is required to provide adequate levels and quality of power to meet load demands. The issue of power quality is significant as it directly affects the characteristics of the microgrid’s operation. This problem can be defined as an occurrence of short to long periods of inadequate or unstable power outputs by the microgrid. In a stand-alone operation mode, the system voltage and frequency must be established by the microgrid, otherwise the system will collapse due to the variety in the microgrid component characteristics. The harmonic distortion of the output power waveforms is also a serious problem that often occurs because of the high speed operation of the converter switches. The long transient period is a critical issue that is usually caused by changing the operation mode or the load demand. Power sharing among the Distributed Generation units is also an important matter for sharing the load appropriately, particularly given that some renewable energy resources are not available continuously. In a utility connected microgrid, the reliable power quality mainly depends on the regulation of both active and reactive power, because the microgrid’s behaviour is mostly dominated by the bulk power system. Therefore, an optimal power control strategy is proposed in this thesis to improve the quality of the power supply in a microgrid scenario. This controller comprises an inner current control loop and an outer power control loop based on a synchronous reference frame and conventional PI regulators. The power control loop can operate in two modes: voltage-frequency power control mode and active-reactive power control mode. Particle Swarm Optimisation is an intelligent searching algorithm that is applied here for real-time self-tuning of the power control parameters. The voltage-frequency power controller is proposed for an inverter-based Distributed Generation unit in an autonomous operation mode. The results show satisfactory system voltage and frequency, high dynamic response, and an acceptable harmonic distortion level. The active-reactive power controller is adopted for an inverter-based Distributed Generation unit in a utility operation mode. This controller provides excellent regulation of the active and reactive power, in particular when load power has to be shared equally between the microgrid and utility. The voltage-frequency and active-reactive power control modes are used for a microgrid configured from two DG units in an autonomous operation mode. The proposed control strategy maintains the system voltage and frequency within acceptable limits, and injects sustained output power from one DG unit during a load change. The reliability of the system’s operation is investigated through developing a small-signal dynamic model for the microgrid. The results prove that the system was stable for the given operating point and under the proposed power controller. Consequently, this research reveals that the microgrid can successfully operate as a controllable power generation unit to support the utility, thus reducing the dependency on the bulk power system and increasing the market penetration of the micro-sources

MULTI-OBJECTIVE OPTIMAL CAPACITY AND PLACEMENT OF DISTRIBUTED GENERATORS IN THE POWER SYSTEM NETWORKS USING ATOM SEARCH OPTIMIZATION METHOD

Nowadays, renewable energy sources become a significant source of energy in the new millennium. The continuous penetration of dispersed resources of the reactive power into power systems is predicted to introduce new challenges. Power loss mitigation and voltage profile development are the major investigation challenges that recently attracted the attention of researchers in the field of power systems. Distributed generation (DG) is widely preferred because it is a highly effective solution that strengthens the performance of power system networks. This multiobjective function study aims to minimise power losses in the feeders, sustain voltage levels and reduce the application cost of DGs by adapting the atom search optimisation simulated on MATLAB software. Two different IEEE power test systems, namely, a 33 bus radial distribution system (RDS) and a 14-bus power system that hosts 1, 2 and 3 DGs in both systems, are demonstrated in this research. Correspondingly, backward–forward sweep and Newton–Raphson power flow methods are used for each system. The proposed technique is compared with the genetic algorithm particle swarm optimisation (GA-PSO) method. Results depict the effectiveness of the proposed method in minimising system power losses and in regulating the voltage profile where the power loss reduction is 25.38% in the 33 bus RDS using 2 DGs. By contrast, the power loss reduction percentages in the 14 bus system are 0.316% and 0.169% in systems with 1 and 2 DGs, respectively. The voltage profile has been enhanced compared with those in the original case and the results obtained from the GA-PSO method

Stochastic power system optimisation algorithm with applications to distributed generation integration

PhD ThesisThe ever increasing level of penetration of Distributed Generation (DG) in power distribution networks is not without its challenges for network planners and operators. Some of these challenges are in the areas of voltage regulation, increase of network fault levels and the disturbance to the network protection settings. Distributed generation can be beneficial to both electricity consumers and if the integration is properly engineered the energy utility. Thus, the need for tools considering these challenges for the optimal placement and sizing of DG units cannot be over emphasized. This dissertation focuses on the application of a soft computing technique based on a stochastic optimisation algorithm (Particle Swarm Optimisation or PSO) for the integration of DG in a power distribution network. The proposed algorithm takes into consideration the inherent nature of the control variables that comprise the search space in the optimal DG sizing/location optimisation problem, without compromising the network operational constraints. The developments of the proposed Multi-Search PSO algorithm (MSPSO) is described, and the algorithm is tested using a standard, benchmarking 69-bus radial distribution network. MSPSO results and performance are compared with that of a conventional PSO algorithm (and other analytical and stochastic methods). Both single-objective (minimising network power loss) and multi-objective (considering nodal voltages as part of the cost function) optimisation studies were conducted. When compared with previously published studies, the proposed MSPSO algorithm produces more realistic results since it accounts for the discrete sizes of commercially available DG units. The new MSPSO algorithm was also found to be the most computationally efficient, substantially reducing the search space and hence the computational cost of the algorithm compared with other methods, without loss of quality in the obtained solutions. As well as the size and location of DG units, these studies considered the operation of the generators to provide ancillary voltage support to the network (i.e. with the generators operating over a realistic range of lagging power factors, injecting reactive power into the network). The algorithm was also employed to optimise the integration of induction generation based DG into the network, considering network short-circuit current ratings and line loading constraints. A new method for computing the reactive power requirement of the Abstract V induction generator (based on the machine equivalent circuit) was developed and interfaced with the MSPSO to solve the optimization problem, including the generator shunt compensation capacitors. Finally, the MSPSO was implemented to carry out a DG integration problem for a real distribution network and the results validated using a commercial power system analysis tool (ERACS).Petroleum Technology Development Fund (PTDF) Overseas Scholarship Schem

Optimal location and reactive power injection of wind farms and SVC’s units using voltage indices and PSO

Nowadays, the use of the wind energy has known an important increase because it is clean and cheap. However, many technical issues could occur due to the integration of wind power plants into power grids. As a result, many countries have published grid code requirements that new installed wind turbines have to satisfy in order to facilitate its intergration to electrical networks. Among those requirements, the wind farms must be able to participate to ancillary services for instance voltage regulation and reactive power control. Nevertheless, in case of small wind farms having not the necessary reactive power capability to contribute to reactive power support, Flexible AC Transmission Systems (FACTS) devices could also be used to participate to reactive power support. In this paper, an optimization method based on particle swarm optimization (PSO) technique is presented. This method allows getting the optimal location and reactive power injection of both wind power plants (WPP) and synchronous var compensators (SVC) with the objective to improve the voltage profile and to minimize the active power losses. The IEEE 14 bus system and a 20 MW wind farm based doubly fed induction generator (DFIG) are used to validate the proposed algorithm. The simulation results are analysed and compared

Coordinated two-stage volt/var management in distribution networks

This paper investigates daily volt/var control in distribution networks using feeder capacitors as well as substation capacitors paired with on-load tap changers. A twostage coordinated approach is proposed. Firstly, the feeder capacitor dispatch schedule is determined based on reactive power heuristics. Then, an optimisation model is applied to determine the dispatch schedule of the substation devices taking into account the control actions of the feeder capacitors. The reference voltage of the substation secondary bus and the tap position limits of transformers are modified such that the model adapts to varying load conditions. The optimisation model is solved with a modified particle swarm optimisation algorithm. Furthermore, the proposed method is compared with conventional volt/var control strategies using a distribution network case study. It is demonstrated that the proposed approach performs better than the conventional strategies in terms of voltage deviation and energy loss minimisation