With increasing level of distributed generation¡]DG¡^on radial feeders in electric distribution systems, it could cause over-voltages as well as under-voltages depending on several factors including DG capacity, locations, and the strategy of voltage regulation. This thesis describes the typical and proposed voltage control strategies that could allow the increase of DG interconnection capacity. By using probabilistic load flow technique, voltage regulation performance for cases with different levels of DG outputs, demands and voltage control strategies are presented. They are compared by using a voltage profile improvement index and a risk assessment technique.

Today uncertainties of petroleum supply and concerns over global warming call for further advancement of green vehicles with higher energy efficiency and lower green house gas (GHG) emissions. Development of advanced hybrid electric powertrain technology plays an important role in the green vehicle transformation with continuously improved energy efficiency and diversified energy sources. The added complexity of the multi-discipline based, advanced hybrid powertrain systems make traditional powertrain design method obsolete, inefficient, and ineffective. This research follows the industrial leading model-based design approach for hybrid electric vehicle powertrain development and introduces the optimization based methods to address several key design challenges in hybrid electric powertrain and its control system design. Several advanced optimization methods are applied to identify the proper hybrid powertrain architecture and design its control strategies for better energy efficiency. The newly introduced optimization based methods can considerably alleviate the design challenges, avoid unnecessary design iterations, and improve the quality and efficiency of the powertrain design. The proposed method is tested through the design and development of a prototype extended range electric vehicle (EREV), UVic EcoCAR. Developments of this advanced hybrid vehicle provide a valuable platform for verifying the new design method and obtaining feedbacks to guide the fundamental research on new hybrid powertrain design methodology. / Graduate

In diesel-electric powertrains, the wheels are mechanically decoupled from the internal combustion engine (ICE). The conventional control approach for such a powertrain is to let the driver control the traction motor while the ICE realizes speed control, causing power to be pulled through the powertrain. An alternative approach is to push power forward by letting the driver control the ICE instead. In this thesis, a conceptual simulation model of a diesel-electric powertrain is compiled and the charcteristics of this novel approach investigated. It is concluded that the new approach makes full ICE power utilization possible even with engine performance reductions present, and also that it handles load prioritization in a natural way. However, takeoff from standstill and low-speed driving become difficult due to the effective gear ratio growing towards infinity for decreasing vehicle speed, causing high traction torques at low speed.

Bangkok is a city where the congestion levels have been a major problem for many years. In 2017, Bangkok was rated the most congested city in Asia, and the second most congested in the world. According to The Expressway Authority of Thailand (EXAT), on-ramp merging is one of the most critical problem that causes congestion on the urban expressways. EXAT have evaluated several merging control strategies through microscopic traffic simulation to find suitable strategies for implementation in real life. However, their simulation studies were all based on the assumption that all motorists strictly follow the traffic rules. This is not the actual case in Bangkok, where the drivers ignore both solid lines and striped areas, as well as utilize the shoulder lane on a regular basis. The aim of this thesis is to investigate if it is possible to include this complex driving behavior in existing microscopic simulation models. A second objective is to identify merging control strategies that can reduce the occurrence and the effects of this driving behavior in order to increase the throughput at an on-ramp area on Sirat Expressway. A model was built in VISSIM and calibrated based on data collected from video recordings. In the study, parameters that are significant for the driving behavior modeling, as well as the difficulties that arise from performing a realistic calibration of the model using video observations and model-specific constraints, are identified. From the video recordings it was discovered that the main problem causing the congestion was a result of the mainline traffic who traversed to the on-ramp. Two merging control strategies were suggested to address this problem: the installment of a center barrier, and successive merging areas. The results confirmed that both actions can improve the traffic situation in terms of reducing the individual travel time. Installing a center barrier was the most efficient option and reduced the travel time by 16.58 % on the mainline and 63.24 % at the on-ramp.

Wastewater compose an environmental risk as it contains high levels of nutrients, including phosphorus. Wastewater treatment plants (WWTPs) reduce phosphorus by using coagulants that precipitate soluble phosphate into metal phosphate, which is separated by settling. Coagulant flow is regulated by a control strategy, typically feedforward or feedback control. Feedforward is based on incoming wastewater disturbances whereas feedback control uses outgoing process values. Incoming phosphate is hard to measure and can be estimated using soft sensors. Modelling control strategies can help decide which strategy that is most suitable. Models describing phosphorus removal are Activated Sludge Model, ASM2d, and primary clarifier model. ASM2d models phosphorus precipitation and the primary clarifier model settling of particles. Tivoli WWTP faces challenges to reach effluent requirements of phosphorus. The wastewater flows through an equalisation tank, Regnbågen, before being pumped to Tivoli. Particulate matter settles in Regnbågen, which is removed by reducing the water level in Regnbågen. This rapidly increases incoming particulate load to Tivoli.Tivoli’s current control strategy is feedforward proportional to suspended solids. It is suspected, that this strategy overdose coagulant during the emptying of Regnbågen. The purpose of this thesis was to find the optimal control strategy for phosphorus precipitation at Tivoli WWTP, by using a model-based approach. Control strategies modelled are; feedforward, feedback and these two control strategies combined. Additional issues resolved are 1) calibration of a model that predicts the effect of chemical dosage on effluent phosphorus concentration from the primary clarifier, 2) calibrationof a soft sensor, 3) determination of which control strategy that is most suitable. ASM2d and a primary clarifier model were used to create a model describing chemical phosphorus removal. The calibration matches measured phosphate concentration, but underestimate peaks. The primary clarifier model was calibrated by minimising load differences for phosphate and total suspended solids, and was calibrated satisfyingly. A simplified soft sensor was constructed, described by a linear relationship between phosphate and pH. Three disturbances for feedforward control were analysed; measured phosphate, the soft sensors estimation of phosphate and Tivoli’s current controlstrategy. The optimal control strategy was found through a multi-criteria analysis. The optimal control strategy is the combined control strategy, when feedforward is proportional to incoming measured phosphate. The performance of all analysed feedforward disturbances were significantly improved when combined with feedback control. Furthermore, consequential errors are distinct when the soft sensor miss-predictincoming phosphate concentration. If the phosphate concentration cannot be correctly measured/estimated, feedback control alone has the best performance.

This contribution presents an operating strategy for a novel valve structure for mobile machines’ working hydraulics which combines the flexibility and energetic benefits of individual metering with the functionality of common primary pressure compensation (IPC). The aim is to set up a system that uses a minimal amount of sensors and simple control algorithms. A control strategy theoretically described in /1/ is modified to facilitate the practical implementation on a mini excavator implement as a test rig. This test rig consists only of components that are currently available off-the-shelf to show that it is possible to develop an individual metering system under these economic restrictions. The novel is more energy efficient than common flow sharing systems but provides the same functionality. The control algorithm is experimentally evaluated in terms of functionality and energy consumption. Simulations show potential for further improvements.

Energy saving and fuel consumption reduction techniques are among the principal interests for both academic institutions and industries, in particular, system optimization and hybridization. This paper presents a new hydraulic hybrid system layout for mobile machinery implemented on a middle size excavator. The hybridization procedure took advantage of a dynamic programming (DP) algorithm, which was also utilized for the hybrid components dimensioning and control strategy definition. A dedicated experimental activity on test bench was performed on the main components of the energy recovery system (ERS). The JCMAS working cycle was considered as the reference test for a fuel consumption comparison between the standard and the hybrid excavator. A fuel saving up to 8% on the JCMAS cycle, and up to 11% during the digging cycle, has been allowed by the proposed hybrid system.

In this thesis, we investigated if reinforcement learning could be applied on elevator systems to improve performance. The performance was evaluated by the average squared waiting time for the passengers, and the buildings considered were apartment buildings. The problem of scheduling elevator cars is an NP-hard problem, and no optimal solution is known. Therefore, an approach where the system learns a strategy instead of using a heuristic, should be the easiest way to get near an optimal solution. A learning system was constructed, where the system was trained to use the best scheduling algorithm out of five in a given situation, based on the prevailing traffic. The purpose of this approach was to reduce the training time that was required in order to get good performance and to lower the complexity of the system. A simulator was then developed, in which the different algorithms were implemented and tested in four different scenarios, where the size of the building and the number of elevator cars varied. The results generated by the simulator showed that reinforcement learning is a great strategy to use in buildings with 16 floors and three or four elevator cars. However, reinforcement learning did not increase the performance in buildings with 10 floors and two to three elevator cars. A possible reason for this is that the variation in performance between the different scheduling algorithms was too small in these scenarios. / I denna rapport har vi undersökt huruvida reinforcement learning är användbart för att öka prestandan för hissystem i lägenhetshus. Prestandan bedömdes efter de genomsnittliga kvadrerade väntetiderna för resenärerna. Schemaläggningsproblemet för hissar är NP-svårt och ingen optimal lösning är känd. Att lösa problemet med hjälp av ett system som lär sig hur det ska agera, bör således vara en enklare strategi för att komma nära den optimala lösningen än att använda sig av en heuristik. Ett självlärande system konstruerades, där systemet tränades att använda den bäst lämpade schemaläggningsalgoritmen med avseende på rådande trafikförhållanden. Det fanns totalt fem olika algoritmer att välja bland. Anledningen till att detta gjordes i stället för att systemet skulle lära sig en komplett strategi, var för att sänka träningstiden som krävdes för att åstadkomma bra resultat och för att minska komplexiteten. En simulator utvecklades sedan, där de olika algoritmerna implementerades och testades på fyra olika scenarion, där storleken på byggnaden och antalet hissar varierade. Resultaten som genererades visade att reinforcement learning fungerar utmärkt på byggnader med 16 våningar och tre eller fyra hissar. På byggnader med tio våningar och två till tre hissar är det dock inte lika användbart och där bör i stället enklare algoritmer användas. En möjlig förklaring till detta är att prestandaskillnaderna mellan algoritmerna var för små under dessa scenarion.

The cooperation between large scale Concentrated Solar Power plants (CSP) and Solar Photovoltaic (PV) parks can offer stability in power supply and enhance the capacity factor of the CSP plant intended to cover a common demand on the power system. Moreover, it can offer an investment option with lower risk. This Master thesis project presents optimum plant configurations for both technologies under the same meteorological and market conditions. The study is based in the South African electricity market and the Renewable Energy Independent Power Producer Program currently in place in the country. Using MATLAB and TRNSYS softwares, a series of detailed codes were designed in order to model both technologies energy transformation process. The main approach was to design the nominal operation point of both technologies for a given typical meteorological year data and respective technical conditions for each case. Then, a transient simulation was done in order to obtain the electricity yield. The intention was to measure the internal rate of return, levelized cost of electricity and capacity factor for each technology and the combined configuration (CSP-PV plant) under different scenarios and operation modes while a firm capacity was maintained. It was found that the plants can be economically feasible by sizing a storage unit capable of just covering the peak hours. The solar multiple sizes can vary depending on the scenario and plant configuration. Moreover, the internal rate of return increases with the capacity of the CSP in all cases. After the results were obtained, a comparison with a single CSP plant and the optimum CSP-PV plant was done in order to evaluate the performance of the proposed cooperation. Even though the internal rate of return of the CSP-PV plant was found to be within a good range for investment, the CSP-alone alternative offered always higher internal rate of return and lower levelized cost of electricity values. Nonetheless, it was found that the capacity factor of the combined configuration was favored by the integration of PV. The PV alone configuration hold the lowest levelized cost of electricity, thus considered the best option for and investment in South Africa due to its independence towards incentives. Combined PV-CSP systems were also found to be an attractive investment under the South African scheme if the CSP capacity is similar to the PV power plant.

This work deals with the problem of choosing the correct heating curve for a certain building package (envelope plus distribution system). This topic is particularly relevant in countries like Sweden where heating curve is the most common way to control heat pumps. The analysis, involving four building models with respective distribution systems (two have floor heating and two radiators) and a variable speed GSHP, shows how, for a fixed location, the proper heating curve changes considering different building envelopes and different emitters. It is highlighted, therefore, how the adoption of a generic heating curve for all the buildings can cause discomfort and energy inefficiency. An algorithm to adjust the curve is then presented, and the results are compared with the reference case. The algorithm manages to improve comfort considerably and, for the A-class building, also SPF increases a lot (12.5%). The whole study was performed by means of TRNSYS® neglecting the DHW demand. / EffSys Expand P18: Smart Control Strategies for Heat Pump Systems