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The requirement of accommodating more distributed generations (DGs) at distribution voltage level has encouraged distribution network operators to utilize their feeder capacity more effectively. This requires the availability of the various network measurements such as voltages, currents, loads, voltage control settings and DG outputs. However these quantities are not directly available in the distribution network control centre. Therefore, to control voltage and power flow in the network, the estimations of these quantities are required. This consists of the monitoring and control of the network operation by application of modern distribution management system (DMS) at the primary substation. The state estimator takes all the available network measurement information, together with a parameterized network model and estimates state of the system in operational time scale. The estimator outputs are then fed into the main control functions and other asset management tools. Although the application of State Estimation (SE) is very common task at transmission system, the practical application are not common at distribution level. This is due to the fact that the operation, topology and design at distribution level differ from those at transmission level. The untransposed three-phase circuits, unbalanced loads, shorter lines with higher ratio of R/X, and the existence of discrete control options such as transformer tap positions illustrates some of those differences. These specificities have motivated this piece of research to consider some of the key issues in distribution system state estimation and to develop algorithms to tackle them. This thesis investigated in detail the criterion for identifying suitable solvers for the distribution system state estimation (DSSE) while considering the specific characteristic of the distribution network such as discrete tap position by exploring new optimization methods which are likely to be useful for practical implementation. Some of the research findings have already been disseminated through invited conference panel and IEEE journal.

The emerging paradigm shift towards the Smart Grid concept, has vigorously encouraged the broad deployment of distributed energy resources (DER), such as energy storage (ES) and flexible demand (FD) and renewable micro-generators, in the energy system. In deregulated power systems, the deployment of flexibility pertaining to ES and FD is associated with their efficient integration in the electricity market. However, significant participation barriers have triggered the introduction of distributed energy resources (DER) aggregators in electricity markets, which settle the necessary framework for the market realisation of their promising operational flexibility potential. The significant number and diversity of resources pertaining to the DER aggregator portfolio, combined with multiple stochastic components affecting its optimal operation demonstrate a high-dimensional stochastic problem. Existing literature focusing on the problem of the optimal operation of DER aggregators exhibits significant limitations, since two-stage stochastic formulations are adopted. In this context, this thesis proposes, analyses and evaluates a novel multistage stochastic model, where multidimensional stochasticity is efficiently considered. Suitable dimensionality reduction and decomposition techniques have been deployed to tackle the computational issues stemming from the high dimensionality of the problem. Stochastic Dual Dynamic programming (SDDP) is deployed to alleviate computational tractability problems. Autoregressive models (AR) are employed to articulate temporal and cross-variable dependencies among the stochastic variables. Two novel extensions of the traditional SDDP algorithm, where linear (i.e. AR) models are integrated in the solution process and enhance solution quality, are proposed. A simulation framework for the validation and assessment of the proposed extended SDDP models, which compares them against scenario tree formulations with different structural characteristics, is presented. Case studies demonstrate that the extended SDDP models achieve a better trade-off between solution efficiency and computational performance. Additionally, results highlight the value of strategic positioning of the DER aggregator portfolio, when limited renewable generation is available. Finally, the effect of strategic decision-making based on less accurate information is shown to be intensified when the aggregator manages a more flexible portfolio.

A sensor network is a distributed system, consisting of plenty of embedded sensors that can be deployed over a large-scale physical environment. One of the major applications of sensor networks is to monitor the state of systems that are evolving in the sensing field. Thanks to the emergence of advancements in high-performance processors, nodes in a sensor network can not only collect measurements but coordinate to estimate the state of systems as well. This thesis proposes a monitoring architecture, where distributed state estimation and fault detection algorithms are implemented by every node in the sensor networks to track the system’s state while simultaneously detecting the faults occurred in either the monitored systems or the sensor networks. Several approaches for different monitoring tasks are presented in this thesis and classified into two main parts: distributed state estimation and fault detection algorithms in the monitoring architecture. In the first part, we present two distributed state estimation algorithms in the sensor networks for the monitoring of a system, which can be described by a centralized, decentralized, or distributed dynamic model. The first one is implemented over a sensor network, where the local estimator in each node consists of a filtering step – which uses a weighted combination of neighboring sensors information – and a model-based state predictor. The filtering weights and prediction parameters jointly minimize both the mean and variance of the prediction error in a Pareto optimization framework at each time step. Since each predictor uses the model of the whole system monitored by the sensor network, the algorithm over a sensor network can only monitor a centralized system or each subsystem of a decentralized system. For a distributed system, where several subsystems interact with each other, the second algorithm implemented over several sensor networks is introduced so that each sensor network can coordinate with neighboring networks to monitor the corresponding subsystem of the distributed system. The second part of the thesis is devoted to fault detection algorithms for process faults in monitored systems and sensor faults in sensor networks. The aforementioned estimation algorithm over a sensor network is applied to design process fault detection algorithm for a centralized or decentralized system. A residual is defined, and suitable stochastic thresholds are designed, allowing to set the parameters so to guarantee an upper bound of false alarms probability. For detecting sensor faults in the sensor networks, the centralized, decentralized, and distributed sensor fault detection schemes are proposed in a discrete-time framework. And the detection performance is compared by an industrial benchmark simulation in a continuous stirred tank heater (CSTH) pilot plant. Then a rigorous fault detectability and detection time interval analysis of the centralized sensor fault detection scheme is presented. The performance of proposed distributed estimation methods and effectiveness of presented fault detection methods are evaluated by extensive numerical and industrial benchmark simulations.

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