In recent year, the awareness of environmental protection has made the power dispatch problem not necessarily economy-oriented. This thesis proposed the application of Particle Swarm Optimization (PSO) algorithm to solve the Unit Commitment (UC) problem for 24 hours with maximum profit in the power and carbon market. Optimal Power Flow (OPF) is used to solve the UC problem for the interconnected power network that is comprised of three independent areas to optimize the dispatching strategy. The UC problem must satisfy the constraints of the load demand, generating limits, minimum up/down time, ramp rate limits, and also the limits of power flow, buses voltage and transmission line capacity. The other objective of this thesis is to employ the Static Synchronous Series Compensator (SSSC) to integrate with OPF based on Equivalent Current Injection (ECI) power flow model, and install it at interconnected lines between each independent area controlling the power flow to reduce emission. In order to avoid the local optimality problem, this thesis proposed the utilization of the Multiple Particle Swarm Optimization (MPSO), which can quickly reach the optimal solution with a better performance and accuracy. The Independent Power Producer (IPP) can get the maximum profit with installed SSSC from the power and carbon trading with the calculation of power wheeling expense and carbon forecasting data. Furthermore, it can also assess the need of participating in the trading market or not.

In recent years, with the energy shortage, the use of renewable energy is inevitable. With CO2 the most important greenhouse gas causing global warming as well as the increase of population, renewable energy is one way to save energy and reduce carbon emissions. The traditional capacity investment for serving the load in distribution systems usually considered the addition of new substations or expansion of the existing substation and associated new feeder requirement. Nowadays, there are a lots of distributed generations (DG¡¦s) to be chosen. Factors of the choice taken into account will include lower pollution, higher efficiency, higher return rate for construction of distributed power generation systems. This thesis assumes that the distributed generation can be invested for long-term power plant planning. The planning of DG would be investigated from the perspectives of the independent investors. The modified Particle Swarm Optimization is proposed to determine the optimal sizing and sit of DG¡¦s addition in distribution systems with the constrains of CO2 limitation and addition of distributed generation to maximize profits. This thesis deals with discrete programming problem of optimal power flow, which includes continuous and discrete types of variables. The continuous variables are the generating unit real power output and the bus voltage magnitudes, the discrete variables are the shunt capacitor banks and sit problems. The Miaoli-Houlong system of Taiwan power will be used in this thesis for the verification of the feasibility of the proposed method.

The ¡§Congestion Management¡¨ (CM) always has been an outstanding and major problem in power system operation. To solve this problem, experts compose solutions in a wide variety. This thesis, based on the equivalent current, applies the Equivalent Current Injection (ECI) concept and circuit parameters to derive the Power Flow Tracing Method (PFTM) . By means of this method we can get a Sensitive Matrix (SM), which is also called the Contribution Matrix (CM), to show the linear relationship between the input power and tidal current discharge of each generator set, with the linear relationship we can derive the mathematic model of treating the congestion problem discussed in this thesis. Combining the Predictor-Corrector Interior Point Algorithm (PCIPA), we can manipulate the change of each generator set in the prospective of solving the congestion problem resulting from the system contingency (SC). The thesis performed various simulations for the IEEE 30 Bus system. Regarding the power contingencies, the solutions of the power-congestion problems can be resulted from the following incidents: heavy load addition, transmission line tripped, generator malfunction as well as the multi-contingencies, etc., which can all be solved with solutions within reasonably restricted domains. We can thus verify the effectiveness of the method .

With the deregulation of power industry and the market competition, low cost, reliable power supply, and secured system operations are major concerns of the advanced deregulation markets. Power system protection is important for service reliability and quality assurance. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively and accurately with fault alarms. First, an operational strategy for secondary power system restoration using Modified Grey Relational Analysis (MGRA) is proposed. The Restoration Scheme (RS) can be divided into three steps involving fault section determination, recovering process, and voltage correction process. Three GRAs are incorporated to design the overall restoration scheme. The first GRA uses network switching status to identify the fault. The second GRA combines switching states and load levels for network recovery. The third GRA uses capacitor bank control to support bus voltages. For security operation of restoration scheme, an Equivalent Current Injection (ECI) based hybrid current-power Optimal Power Flow (OPF) model with Predictor-Corrector Interior Point Algorithm (PCIPA) is used to verify the proposed scheme by off-line analysis to confirm a secure overall network operation including load-power balance, power generation limits, voltage limits, and power flow limits. The proposed method can further decompose into two sub-problems. Computer simulations were conducted with an IEEE 30-bus power system to show the effectiveness of the proposed restoration scheme and the PCIPA technique is very accurate, robust, and efficient for the modified OPF solution.

<p>This thesis is based on studies of the deregulated electricity markets located in the United States of America. The problem statement of the thesis evolved continuously throughout our initial period of research. Focus was finally put on monitoring and detection of potential market power caused by congestion in the transmission network. The existence of market power is a serious concern in today’s electric energy markets. A system that monitors the trading is needed and much research and many proposals on how to deal with this problem have been introduced over the years. We focus on some of these approaches and develop an approach of our own, which we call “Monopolistic Energy Calculation”. We adopt the idea to identify participants with the ability to raise prices without losing market share. An ability that should not be present on a competitive market. We take this idea further by identifying participants with the ability to make considerable price raises without losing all market shares. We propose a way to calculate the remaining market shares (Monopolistic Energy Levels) after a large price raise. These calculated levels of energy, that are only deliverable by a certain participant or by a certain group of participants, are caused by the active congestions in the network.</p><p>The approach detects the amounts of these energy levels and the location in the network at which they are present. This is a prospective method if used with a prediction of the following day’s demand, which is regularly available with high accuracy. The method can also be used for monitoring purposes to identify critical situations in real-time. The method is implemented and two sets of simulations are done in which we explain and evaluate the approach. The results are promising and the correlation between “Monopolistic Energy” and market power is confirmed.</p> / <p>Detta examensarbete är baserat på studier av de deregulerade electricitsmarknaderna i USA. Problemformuleringen var i början av detta arbete inte definitiv, utan utvecklades under en längre inledande fas av forskningsarbete. Slutligen kunde vi faställa att detektion av potentiell marknadskraft på elektricitetsmarknaden, orsakat av överbelastningar i transmissionnätverket, var av särskilt intresse. Ett system som övervakar handeln och förekomster av orättvisor orsakat av detta är nödvändigt. Det har de senaste åren gjorts mycket forskning inom detta område. Baserat på denna forskning utvecklades sedan ett eget förslag, som vi kallar ”Monopolistic Energy Calculations”. Vissa tidigare förslag på hur problemet kan angripas blev av särskilt intresse. En idé från dessa var att identifiera marknadsaktörer med förmågan att höja priser utan att förlora marknadsandelar, en icke önskvärd egenskap hos aktörer då en konkurrenskraftig marknad är eftertraktad.</p><p>Vi tar denna idé ett steg längre genom att identifiera marknadsaktörer med förmågan att höja priser signifikant utan att förlora alla marknadsandelar. Vi föreslår ett sätt att beräkna dessa energinivåer som endast är möjliga att levereras av en eller ett fåtal särskilda aktörer, som direkt följd av de aktiva stockningarna i nätverket, under antagandet av en inelastisk efterfrågan. Vi föreslår ett sätt att beräkna de återstående marknadsandelarna (Monopolistic Energy Levels) efter en stor prishöjning. Vår metod beräknar mängden av denna energi och var i nätverket dessa mängder förekommer. Denna metod kan sia om framtida problem om en estimering av morgondagens efterfråga används. Sådana estimeringar görs idag</p><p>regelbundet med hög träffsäkerhet. Metoden kan även användas i realtid för upptäckt av kritiska marknadssituationer. Simuleringar av detta görs som förklarar vår lösning och utvärderar den. Resultaten är lovande och korrelationen mellan ”Monopolistisk Energi” och marknadskraft är bekräftade.</p>

The contributions of the research are (a) an infrastructure of data acquisition systems that provides the necessary information for an automated EMS system enabling autonomous distributed state estimation, model validation, simplified protection, and seamless integration of other EMS applications, (b) an object-oriented, interoperable, and unified component model that can be seamlessly integrated with a variety of applications of the EMS, (c) a distributed dynamic state estimator (DDSE) based on the proposed data acquisition system and the object-oriented, interoperable, and unified component model, (d) a physically-based synchronous machine model, which is expressed in terms of the actual self and mutual inductances of the synchronous machine windings as a function of rotor position, for the purpose of synchronous machine parameters identification, and (e) a robust and highly efficient algorithm for the optimal power flow (OPF) problem, one of the most important applications of the EMS, based on the validated states and models of the power system provided by the proposed DDSE.

This thesis is based on studies of the deregulated electricity markets located in the United States of America. The problem statement of the thesis evolved continuously throughout our initial period of research. Focus was finally put on monitoring and detection of potential market power caused by congestion in the transmission network. The existence of market power is a serious concern in today’s electric energy markets. A system that monitors the trading is needed and much research and many proposals on how to deal with this problem have been introduced over the years. We focus on some of these approaches and develop an approach of our own, which we call “Monopolistic Energy Calculation”. We adopt the idea to identify participants with the ability to raise prices without losing market share. An ability that should not be present on a competitive market. We take this idea further by identifying participants with the ability to make considerable price raises without losing all market shares. We propose a way to calculate the remaining market shares (Monopolistic Energy Levels) after a large price raise. These calculated levels of energy, that are only deliverable by a certain participant or by a certain group of participants, are caused by the active congestions in the network. The approach detects the amounts of these energy levels and the location in the network at which they are present. This is a prospective method if used with a prediction of the following day’s demand, which is regularly available with high accuracy. The method can also be used for monitoring purposes to identify critical situations in real-time. The method is implemented and two sets of simulations are done in which we explain and evaluate the approach. The results are promising and the correlation between “Monopolistic Energy” and market power is confirmed. / Detta examensarbete är baserat på studier av de deregulerade electricitsmarknaderna i USA. Problemformuleringen var i början av detta arbete inte definitiv, utan utvecklades under en längre inledande fas av forskningsarbete. Slutligen kunde vi faställa att detektion av potentiell marknadskraft på elektricitetsmarknaden, orsakat av överbelastningar i transmissionnätverket, var av särskilt intresse. Ett system som övervakar handeln och förekomster av orättvisor orsakat av detta är nödvändigt. Det har de senaste åren gjorts mycket forskning inom detta område. Baserat på denna forskning utvecklades sedan ett eget förslag, som vi kallar ”Monopolistic Energy Calculations”. Vissa tidigare förslag på hur problemet kan angripas blev av särskilt intresse. En idé från dessa var att identifiera marknadsaktörer med förmågan att höja priser utan att förlora marknadsandelar, en icke önskvärd egenskap hos aktörer då en konkurrenskraftig marknad är eftertraktad. Vi tar denna idé ett steg längre genom att identifiera marknadsaktörer med förmågan att höja priser signifikant utan att förlora alla marknadsandelar. Vi föreslår ett sätt att beräkna dessa energinivåer som endast är möjliga att levereras av en eller ett fåtal särskilda aktörer, som direkt följd av de aktiva stockningarna i nätverket, under antagandet av en inelastisk efterfrågan. Vi föreslår ett sätt att beräkna de återstående marknadsandelarna (Monopolistic Energy Levels) efter en stor prishöjning. Vår metod beräknar mängden av denna energi och var i nätverket dessa mängder förekommer. Denna metod kan sia om framtida problem om en estimering av morgondagens efterfråga används. Sådana estimeringar görs idag regelbundet med hög träffsäkerhet. Metoden kan även användas i realtid för upptäckt av kritiska marknadssituationer. Simuleringar av detta görs som förklarar vår lösning och utvärderar den. Resultaten är lovande och korrelationen mellan ”Monopolistisk Energi” och marknadskraft är bekräftade.

Grid-connected photovoltaic (PV) capacity is increasing worldwide, mainly due to extensive subsidy schemes for renewable electricity generation. A majority of newly installed systems are distributed small-scale systems located in distribution grids, often at residential customers. Recent developments suggest that such distributed PV generation (PV-DG) could gain more interest in Sweden in the near future. With prospects of decreasing system prices, an extensive integration does not seem impossible. In this PhD thesis the opportunities for utilisation of on-site PV generation and the consequences of a widespread introduction are studied. The specific aims are to improve modelling of residential electricity demand to provide a basis for simulations, to study load matching and grid interaction of on-site PV and to add to the understanding of power system impacts. Time-use data (TUD) provided a realistic basis for residential load modelling. Both a deterministic and a stochastic approach for generating different types of end-use profiles were developed. The models are capable of realistically reproducing important electric load properties such as diurnal and seasonal variations, short time-scale fluctuations and random load coincidence. The load matching capability of residential on-site PV was found to be low by default but possible to improve to some extent by different measures. Net metering reduces the economic effects of the mismatch and has a decisive impact on the production value and on the system sizes that are reasonable to install for a small-scale producer. Impacts of large-scale PV-DG on low-voltage (LV) grids and on the national power system were studied. Power flow studies showed that voltage rise in LV grids is not a limiting factor for integration of PV-DG. Variability and correlations with large-scale wind power were determined using a scenario for large-scale building-mounted PV. Profound impacts on the power system were found only for the most extreme scenarios. / Felaktigt tryckt som Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 711

The deployment of Renewable Energy (RE)-based generation has experienced a sustained global growth in the recent decades, driven by many countries' interest in reducing greenhouse gas emissions and dependence on fossil fuel for electricity generation. This trend is also observed in remote off-grid systems (isolated microgrids), where local communities, in an attempt to reduce fossil fuel dependency and associated economic and environmental costs, and to increase availability of electricity, are favouring the installation of RE-based generation. This practice has posed several challenges to the operation of such systems, due to the intermittent and hard-to-predict nature of RE sources. In particular, this thesis addresses the problem of reliable and economic dispatch of isolated microgrids, also known as the energy management problem, considering the uncertain nature of those RE sources, as well as loads. Isolated microgrids feature characteristics similar to those of distribution systems, in terms of unbalanced power flows, significant voltage drops and high power losses. For this reason, detailed three-phase mathematical models of the microgrid system and components are presented here, in order to account for the impact of unbalanced system conditions on the optimal operation of the microgrid. Also, simplified three-phase models of Distributed Energy Resources (DERs) are developed to reduce the level of complexity in small units that have limited impact on the optimal operation of the system, thus reducing the number of equations and variables of the problem. The proposed mathematical models are then used to formulate a novel energy management problem for isolated microgrids, as a deterministic, multi-period, Mixed-Integer Nonlinear Programming (MINLP) problem. The multi-period formulation allows for a proper management of energy storage resources and multi-period constraints associated with the commitment decisions of DERs. In order to obtain solutions of the energy management problem in reasonable computational times for real-time, realistic applications, and to address the uncertainty issues, the proposed MINLP formulation is decomposed into a Mixed-Integer Linear Programming (MILP) problem, and a Nonlinear programming (NLP) problem, in the context of a Model Predictive Control (MPC) approach. The MILP formulation determines the unit commitment decisions of DERs using a simplified model of the network, whereas the NLP formulation calculates the detailed three-phase dispatch of the units, knowing the commitment status. A feedback signal is generated by the NLP if additional units are required to correct reactive power problems in the microgrid, triggering a new calculation MINLP problem. The proposed decomposition and calculation routines are used to design a new deterministic Energy Management System (EMS) based on the MPC approach to handle uncertainties; hence, the proposed deterministic EMS is able to handle multi-period constraints, and account for the impact of future system conditions in the current operation of the microgrid. In the proposed methodology, uncertainty associated with the load and RE-based generation is indirectly considered in the EMS by continuously updating the optimal dispatch solution (with a given time-step), based on the most updated information available from suitable forecasting systems. For a more direct modelling of uncertainty in the problem formulation, the MILP part of the energy management problem is re-formulated as a two-stage Stochastic Programming (SP) problem. The proposed novel SP formulation considers that uncertainty can be properly modelled using a finite set of scenarios, which are generated using both a statistical ensembles scenario generation technique and historical data. Using the proposed SP formulation of the MILP problem, the deterministic EMS design is adjusted to produce a novel stochastic EMS. The proposed EMS design is tested in a large, realistic, medium-voltage isolated microgrid test system. For the deterministic case, the results demonstrate the important connection between the microgrid's imbalance, reactive power requirements and optimal dispatch, justifying the need for detailed three-phase models for EMS applications in isolated microgrids. For the stochastic studies, the results show the advantages of using a stochastic MILP formulation to account for uncertainties associated with RE sources, and optimally accommodate system reserves. The computational times in all simulated cases show the feasibility of applying the proposed techniques to real-time, autonomous dispatch of isolated microgrids with variable RE sources.

Combined cooling, heating and power (CCHP) systems are known as trigeneration systems, designed to provide electricity, cooling and heating simultaneously. The CCHP system has become a hot topic for its high system efficiency, high economic efficiency and less greenhouse gas (GHG) emissions in recent years. The efficiency of the CCHP system depends on the appropriate system configuration, operation strategy and facility size. Due to the inherent and inevitable energy waste of the traditional operation strategies, i.e., following the electric load (FEL) and following the thermal load (FTL), more efficient operation strategy should be designed. To achieve the highest system efficiency, facilities in the system should be sized to match with the corresponding operation strategy. In order to reduce the energy waste in traditional operation strategies and improve the system efficiency, two operation strategy design methods and sizing problems are studied (In Chapter 2 and Chapter 3). Most of the improved operation strategies in the literature are based on the ''balance'' plane, which implies the match of the electric demands and thermal demands. However, in more than 95% energy demand patterns, the demands cannot match with each other at this exact ''balance'' plane. To continuously use the ''balance'' concept, in Chapter 2, the system configuration is modified from the one with single absorption chiller to be the one with hybrid chillers and expand the ''balance'' plane to be a ''balance'' space by tuning the electric cooling to cool load ratio. With this new ''balance'' space, an operation strategy is designed and the power generation unit (PGU) capacity is optimized according to the proposed operation strategy to reduce the energy waste and improve the system efficiency. A case study is conducted to verify the feasibility and effectiveness of the proposed operation strategy. In Chapter 3, a more mathematical approach to schedule the energy input and power flow is proposed. By using the concept of energy hub, the CCHP system is modelled in a matrix form. As a result, the whole CCHP system is an input-output model. Setting the objective function to be a weighted summation of primary energy savings (PESs), hourly total cost savings (HTCs) and carbon dioxide emissions reduction (CDER), the optimization problem, constrained by equality and inequality constraints, is solved by the sequential quadratic programming (SQP). The PGU capacity is also sized under the proposed optimal operation strategy. In the case study, compared to FEL and FTL, the proposed optimal operation strategy saves more primary energy and annual total cost, and can be more environmental friendly. Finally, the conclusions of this thesis is summarized and some future work is discussed. / Graduate