Distributed coordination of flexible devices in power networks

The penetration of new types of devices, such as domestic storage and electric vehicles, offers increasing flexibility on demand side. This will bring both new opportunities and challenges to the operation of power systems. The aim of this thesis is to design novel distributed control strategies for large scale coordination of flexible devices. To this end, flexible devices are modelled as self-interested rational agents that aim at minimizing their individual costs in response to the broadcast price signals. This thesis mainly consists of three parts, considering that the price signals can be designed in different forms, and that flexible devices could operate in different markets (e.g. energy markets, and integrated energy and reserve markets). The first part presents a multi-agent framework for the coordination of large populations of micro-storage devices in energy markets, under the assumption that the electricity price is some monotone increasing function of total power demand. The second part extends the work of the first part through taking into account the topology of power networks: the proposed modelling framework envisages heterogeneous groups of loads that operate at different buses, connected by transmission lines of limited capacity. The locational marginal prices of electricity are used as price signals, which are different in general for each bus and calculated through an optimal power flow problem. In the framework of the third part, it is envisioned that micro-storage devices and electric vehicles participate in an integrated energy-reserve market, and that they can contribute to the provision of reserve by being available to reduce their power consumption. These flexible devices autonomously schedule their operation in response to two kinds of price signals - the locational marginal prices of energy and reserve. Iterative schemes for the coordination of the flexible devices are presented in the three parts. It is proved that the proposed coordination schemes can ensure the convergence to stable market configurations, characterized as aggregative equilibria at which each device cannot further reduce its cost by unilaterally changing its power profile. Distributed implementations of these proposed control strategies are discussed, and their performance is evaluated in simulations on large scale power systems.Open Acces

Economic Modeling of Compressed Air Energy Storage

Due to the variable nature of wind resources, the increasing penetration level of wind power will have a significant impact on the operation and planning of the electric power system. Energy storage systems are considered an effective way to compensate for the variability of wind generation. This paper presents a detailed production cost simulation model to evaluate the economic value of compressed air energy storage (CAES) in systems with large-scale wind power generation. The co-optimization of energy and ancillary services markets is implemented in order to analyze the impacts of CAES, not only on energy supply, but also on system operating reserves. Both hourly and 5-minute simulations are considered to capture the economic performance of CAES in the day-ahead (DA) and real-time (RT) markets. The generalized network flow formulation is used to model the characteristics of CAES in detail. The proposed model is applied on a modified IEEE 24-bus reliability test system. The numerical example shows that besides the economic benefits gained through energy arbitrage in the DA market, CAES can also generate significant profits by providing reserves, compensating for wind forecast errors and intra-hour fluctuation, and participating in the RT market

Ultracapacitor storage enabled global MPPT for photovoltaic central inverters

In most large scale grid-tied photovoltaic (PV) plants, central inverter configurations are used, mainly due to higher efficiency and lower cost per kW. However, compared to other configurations its Maximum Power Point Tracking (MPPT) efficiency is the lowest since it is the less distributed configuration. Under non-uniform conditions, as partial shading, several local maxima may arise in the PV curve, hence requiring additional actions to maximize the output power of the PV plant. Moreover the growth of renewable sources in electric markets has tightened grid codes; requiring for PV systems to limit power fluctuations to a certain maximum. This paper presents an alternative to perform Global MPPT (GMPPT) while complying with new grid code limitations for electric markets with high penetration of renewables. The proposed alternative consists on adding an Energy Storage System (ESS) at inverter level. The proposed system consists in an ultracapacitor (UC) bank connected to the DC-link of the central inverter through interleaved DC-DC power converters. The proposed configuration is validated through simulations and tested through extreme conditions. The performance of the system is analyzed and compared to other existing solutions

Minimising the expectation value of the procurement cost in electricity markets based on the prediction error of energy consumption

In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in two markets are known. The expectation value of the total electricity cost is minimised over two parameters that change the amounts of electricity. Two parameters depend only on the expected unit prices of electricity and the distributions of prediction errors for the electricity demand traded in two markets. That is, even if we do not know the predictions for the electricity demand, we can determine the values of two parameters that minimise the expectation value of the procurement cost of electricity in two popular spot markets. We demonstrate numerically that the estimate of two parameters often results in a small variance of the total electricity cost, and illustrate the usefulness of the proposed procurement method through the analysis of actual data

Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark

Unlocking the Potential of Flexible Energy Resources to Help Balance the Power Grid

Flexible energy resources can help balance the power grid by providing different types of ancillary services. However, the balancing potential of most types of resources is restricted by physical constraints such as the size of their energy buffer, limits on power-ramp rates, or control delays. Using the example of Secondary Frequency Regulation, this paper shows how the flexibility of various resources can be exploited more efficiently by considering multiple resources with complementary physical properties and controlling them in a coordinated way. To this end, optimal adjustable control policies are computed based on robust optimization. Our problem formulation takes into account power ramp-rate constraints explicitly, and accurately models the different timescales and lead times of the energy and reserve markets. Simulations demonstrate that aggregations of select resources can offer significantly more regulation capacity than the resources could provide individually.Comment: arXiv admin note: text overlap with arXiv:1804.0389

Building Blocks: Investment in Renewable and Non-Renewable Technologies

Over the last several years, there has been a nation-wide intensification of policies directed at increasing the level of renewable sources of electricity.  These environmental policy changes have occurred against a backdrop of shifting economic regulation in power markets that has fundamentally redefined the mechanisms through which investors in power plants earn revenues. Rather than base payments upon costs, revenues in many regions are now based upon fluctuating energy prices and, in some cases, supplemental payments for installed capacity. This paper studies the interaction between these two major forces that are currently dominating the economic landscape of the electricity industry.  Using data from the western U.S., we examine how the large-scale expansion of intermittent resources of generation could influence long-run equilibrium prices and investment decisions under differing wholesale power market designs.  We find that as the level of wind penetration increases, the equilibrium investment mix of other resources shifts towards less baseload and more peaking capacity.  As wind penetration increases, an “average” wind producer earns increasingly more revenue under markets with capacity payments than those that base compensation on energy revenues.   Investment; Renewable Energy; Capacity Markets