Planning is an essential part of intelligent behavior and a ubiquitous task for both humans and rational agents. One framework for planning in the presence of uncertainty is probabilistic planning, in which actions are described by a probability distribution over their possible outcomes. Probabilistic planning has been applied to different real-world scenarios such as public health, sustainability and robotics; however, the usage of probabilistic planning in practice is limited due to the poor performance of existing planners. In this thesis, we introduce a novel approach to effectively solve probabilistic planning problems by relaxing them into short-sighted problems. A short-sighted problem is a relaxed problem in which the state space of the original problem is pruned and artificial goals are added to heuristically estimate the cost of reaching an original goal from the pruned states. Differently from previously proposed relaxations, short-sighted problems maintain the original structure of actions and no restrictions are imposed in the maximum number of actions that can be executed. Therefore, the solutions for short-sighted problems take into consideration all the probabilistic outcomes of actions and their probabilities. In this thesis, we also study different criteria to generate short-sighted problems, i.e., how to prune the state space, and the relation between the obtained short-sighted models and previously proposed relaxation approaches. We present different planning algorithms that use short-sighted problems in order to solve probabilistic planning problems. These algorithms iteratively generate and execute optimal policies for short-sighted problems until the goal of the original problem is reached. We also formally analyze the introduced algorithms, focusing on their optimality guarantees with respect to the original probabilistic problem. Finally, this thesis contributes a rich empirical comparison between our algorithms and state-of-the-art probabilistic planners.

The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called “classical” AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time – makespan – of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the hm, additive hm and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time, aimed at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods. Results indicate that some plausible such characteristics have been found, but are not entirely conclusive.

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Wind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased loading on wind turbines. In wind farms, wind turbines* wake affects each other depending on their positions and operation modes. Therefore it becomes essential to optimize the wind farm power production as a whole than to just focus on individual wind turbines. The work presented here develops a hierarchical power optimization algorithm for wind farms. The algorithm includes a cooperative level (or higher level) and an individual level (or lower level) for power coordination and planning in a wind farm. The higher level scheme formulates and solves a quadratic constrained programming problem to allocate power to wind turbines in the farm while considering the aerodynamic effect of the wake interaction among the turbines and the power generation capabilities of the wind turbines. In the lower level, optimization algorithm is based on a leader-follower structure driven by the local pursuit strategy. The local pursuit strategy connects the cooperative level power allocation and the individual level power generation in a leader-follower arrangement. The leader, could be a virtual entity and dictates the overall objective, while the followers are real wind turbines considering realistic constraints, such as tower deflection limits. A nonlinear wind turbine dynamics model is adopted for the low level study with loading and other constraints considered in the optimization. The stability of the algorithm in the low level is analyzed for the wind turbine angular velocity. Simulations are used to show the advantages of the method such as the ability to handle non-square input matrix, non-homogenous dynamics, and scalability in computational cost with rise in the number of wind turbines in the wind farm.

As more and more renewable energy sources like wind and solar power are added to the electricgrid, reliable sources of power like hydropower become more important. Hydropower isabundant in Scandinavia, and helps to maintain a stable and reliable grid with added irregularitiesfrom wind and solar power, as well as more fluctuations in demand. Aside from the reliabilityaspect of hydropower, power producers want to maximize their profit from sold electricity. InSweden, power is bid to the spot market at Nord Pool every day, and a final spot price is decidedwithin the electricity market. There is a different electricity price each hour of the day, so it ismore profitable to generate power during some hours than others.There are many other factors that can change when it is most profitable for a hydropower plant tooperate, like how much local inflow of water there is. Hydropower production is an ideal case forusing optimisation models, and they are widely used throughout industry already. Though theoptimisation calculations are done by a computer, there is a lot of manual work from the spottraders that goes into specifying the inputs to the model, such as local inflow, price forecasts, andperhaps most importantly, market strategy. Due to the large amount of work that needs to be donefor each hydropower plant, many of the smaller power plants are not optimised at all, but are leftto run on an automatic control that typically tries to maintain a constant water level. In Fortum,this is called, VNR, or vattennivåreglering (water level regulation).The purpose of this thesis is to develop an optimisation algorithm for a small hydropower plant,using Fortum owned and operated Båthusströmmen as a test case. An optimisation model is builtin Fortum’s current modelling system and is tested for 2016. In addition, a mathematical model isalso built and tested using GAMS. It is found that by optimising the plant instead of running it onVNR, an increase of about 15-16% in profit could be seen for the year 2016. This is a significantimprovement, and is a strong motivator to being optimising the small hydropower plants.Since the main reason many small hydropower plants are not optimised is because it takes toomuch of employees time, a second phase of this thesis was conducted in conjunction with twoother students, Jenny Möller and Johan Wiklund. The focus of this portion was to develop acentralized controller to automatically optimise the production schedule and communicate withthe central database. This would completely remove the workload from the spot traders, as wellas increase the overall profit of the plant. This thesis describes the results from both the Fortummodel and the GAMS model, as well as the mathematical formulation of the GAMS model. Thebasic structure of the automatic controller is also presented, and more can be read in the thesis byMöller and Wiklund (Möller & Wiklund, 2018). / Tillförlitliga energikällor som vattenkraft blir allt viktigare vart eftersom elkraftsystemet utökasmed fler förnybara energikällor som vindkraft och solenergi. I Norden finns det rikligt medvattenkraft, vilket bidrar till att upprätthålla ett stabilt och pålitligt elnät även med ökadeoregelbundenheter från vindkraft och solkraft samt större variationer i efterfrågan. Bortsett frånvattenkraftens tillförlitlighetsaspekter vill kraftproducenter maximera sin vinst från såld el. ISverige läggs dagligen bud på effektvolym till spotmarknaden Nord Pool och ett slutgiltigtmarknadspris bestäms därefter av elmarknaden. Varje timme under dygnet motsvarar ett enskiltelpris, därmed är det mer lönsamt att generera effekt under de timmar där priset är som högst.Det finns många andra faktorer som påverkar när det är mest lönsamt för ett vattenkraftverk attproducera el, exempelvis hur stort det lokala inflödet av vatten är. Vattenkraftproduktion är idealtför tillämpning av optimeringsmodeller, vilka är vanligt förekommande inom verksamhetsområdet.Även om optimeringsberäkningarna utförs av en dator innebär optimeringen mycket manuelltarbete för Fortums elhandlare som specificerar indata till modellen. Exempel på indata är lokaltinflöde, prisprognoser och kanske viktigast av allt marknadsstrategi. På grund av den storamängden arbete som fordras för varje vattenkraftverk, optimeras inte produktionen för många avde småskaliga kraftverken utan de regleras automatiskt med mål att upprätthålla en konstantvattennivå. Denna typ av reglering kallas vattennivåreglering, VNR.Syftet med examensarbetet var att utveckla en optimeringsalgoritm för ett småskaligtvattenkraftverk, där Fortumägda vattenkraftverket Båthusströmmen används som testobjekt. Enoptimeringsmodell utvecklades i Fortums befintliga system och testades för 2016. Dessutom haren matematisk modell utvecklats och testades med GAMS. Det konstaterades att genom attoptimera produktionen från vattenkraftverket istället för att reglera den via VNR kan envinstökning med cirka 15-16 % för noteras år 2016. Detta är en väsentlig förbättring och är ettstarkt argument för att optimera produktionen från småskaliga vattenkraftverk.Eftersom den främsta orsaken till att många småskaliga vattenkraftverk inte optimeras är denutökade arbetsbelastningen det skulle innebära för de anställda, genomfördes en andra fas iexamensarbetet i samverkan med två andra studenter, Jenny Möller och Johan Wiklund. Fokus fördenna del var att utveckla en centraliserad styrenhet för att automatiskt optimera produktionsplaneroch kommunicera med det befintliga centrala systemet. Detta innebär att utökad arbetsbelastningenfrån elhandlarna undviks, samt öka vattenkraftverkets totala vinst. Denna rapport beskriverresultaten från både Fortum-modellen och GAMS-modellen, liksom den matematiskaformuleringen av GAMS-modellen. Även grundstrukturen för det självreglerandeoptimeringsverktyget presenteras, mer kan läsas i rapporten av Möller och Wiklund (Möller &Wiklund, 2018).Nyckelord: Optimering, vattenkraftplanering, självreglerande, automatisk styrning, optimalplanering

Classical planning is the problem of nding a sequence of actions that take an agent from an initial state to a desired goal situation, assuming deter- ministic outcomes for actions and perfect information. Satis cing planning seeks to quickly nd low-cost solutions with no guarantees of optimality. The most e ective approach for satis cing planning has proved to be heuristic search using non-admissible heuristics. In this thesis, we introduce several such heuristics that are able to take into account costs on actions, and there- fore try to minimize the more general metric of cost, rather than length, of plans, and investigate their properties and performance. In addition, we show how the problem of planning with soft goals can be compiled into a classical planning problem with costs, a setting in which cost-sensitive heuristics such as those presented here are essential. / La plani caci on cl asica es el problema que consiste en hallar una secuencia de acciones que lleven a un agente desde un estado inicial a un objetivo, asum- iendo resultados determin sticos e informaci on completa. La plani caci on \satis cing" busca encontrar una soluci on de bajo coste, sin garant as de op- timalidad. La b usqueda heur stica guiada por heur sticas no admisibles es el enfoque que ha tenido mas exito. Esta tesis presenta varias heur sticas de ese g enero que consideran costes en las acciones, y por lo tanto encuentran soluciones que minimizan el coste, en lugar de la longitud del plan. Adem as, demostramos que el problema de plani caci on con \soft goals", u objetivos opcionales, se puede reducir a un problema de plani caci on clasica con costes en las acciones, escenario en el que heur sticas sensibles a costes, tal como las aqu presentadas, son esenciales.