Stochastic Learning of Energy System for Data-Driven Control in Manufacturing Process

To overcome the environmental impacts of a manufacturing factory over its life cycle, the role of sustainable energy effectiveness is vital. For this reason, implementing energy conservation technologies to empower energy efficiency has become an important business for the majority of manufacturing plants. Data-driven control setups seem to be a novel idea to handle the energy efficiency of such complex systems, while machine learning is becoming well-known in the system engineering community. In this paper, a new approach together with optimal control application is considered to open promising energy-saving ideas through investigating machines of a factory using machine learning, specifically, Gaussian Processes Regression (GPR), where the model is built by correlating the dynamics, complexity, and interrelated energy consumption recordings. We connect the idea with controlling a manufacturing system energy in an optimized way, where the Model Predictive Control loop delivers optimal solutions for each control time step. In the end, a numerical example is demonstrated to give a clear picture of the proposed modelling method potentials

Pricing Real-time Stochastic Storage Operations

Pricing storage operation in the real-time market under demand and generation stochasticities is considered. A scenario-based stochastic rolling-window dispatch model is formulated for the real-time market, consisting of conventional generators, utility-scale storage, and distributed energy resource aggregators. We show that uniform pricing mechanisms require discriminative out-of-the-market uplifts, making settlements under locational marginal pricing (LMP) discriminative. It is shown that the temporal locational marginal pricing (TLMP) that adds nonuniform shadow prices of ramping and state-of-charge to LMP removes the need for out-of-the-market uplifts. Truthful bidding incentives are also established for price-taking participants under TLMP. Revenue adequacy and uplifts are evaluated in numerical simulations.Comment: 8 pages, 4 figures, 2022 Power Systems Computation Conference (PSCC

Stochastic gradient methods for stochastic model predictive control

We introduce a new stochastic gradient algorithm, SAAGA, and investigate its employment for solving Stochastic MPC problems and multi-stage stochastic optimization programs in general. The method is particularly attractive for scenario-based formulations that involve a large number of scenarios, for which “batch” formulations may become inefficient due to high computational costs. Benefits of the method include cheap computations per iteration and fast convergence due to the sparsity of the proposed problem decomposition

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

In this paper we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenario-based optimisation problems which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst-case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadratically-constrained quadratic problem and its favourable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors

The real-time optimisation of DNO owned storage devices on the LV network for peak reduction

Energy storage is a potential alternative to conventional network reinforcementof the low voltage (LV) distribution network to ensure the grid’s infrastructure remainswithin its operating constraints. This paper presents a study on the control of such storagedevices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, wherethe objective is to achieve the greatest peak reduction in demand, for a given storagedevice specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-pointcontroller and bench marked against a control algorithm with a perfect forecast. A specificcase study, using storage on the LV network, is presented, and the results of each algorithmare compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques

Optimization of multi-temporal generation scheduling in power system under elevated renewable penetrations: A review

The traditional power generation mix and the geographical distribution of units have faced structural reform with the increasing renewables. The existing scheduling schemes confront the optimization challenges of multi-source collaborative and multi-temporal coordination. This paper reviews the optimization of generation scheduling in power systems with renewables integration in different time scales, which are medium- and long-term, short-term and real-time, respectively. First, the scheduling model and method are summarized. The connections and differences of the multi-source mathematic model with uncertainty, as well as the market mechanism, including thermal power, hydroelectric power, wind power, solar energy, and energy storage, are also indicated. Second, the scheduling algorithm and approach are sorted out from the two dimensions of certainty and uncertainty. The innovation and difference in algorithm between the traditional scheduling and the scheduling problem with renewables are presented. Meanwhile, the interaction and coupling relationship among the different time scales are pointed out in each section. The challenges and shortcomings of current research and references future directions are also provided for dispatchers

A Receding Horizon Control Approach For Re-Dispatching Stochastic Heterogeneous Resources Accounting for Grid and Battery Losses

In this paper, we propose a re-dispatch scheme for radial distribution grids hosting stochastic Distributed Energy Resources (DERs) and controllable batteries. At each re-dispatch round, the proposed scheme computes a new dispatch plan that modifies and extends the existing one. To do so, it uses the CoDistFlow algorithm and applies a receding horizon control principle, while accounting for hard time computation constraints that impact on the instantaneous update of a dispatch plan. CoDistFlow handles stochastic DERs and prosumers uncertainties via scenario-based optimization and the non-convexity of the AC Optimal Power Flow by iteratively solving suitably defined convex problems until convergence. We perform numerical evaluations based on real-data, obtained from a real Swiss grid. We show that, with our proposed re-dispatch scheme, the daily dispatch tracking error can decrease more than 80%, even for small battery capacities, and if re-dispatch is frequent enough, it can be eliminated. Finally, we show that re-dispatch should be performed as often as the market allows and the performance continues to improve

Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon

We explore reinforcement learning methods for finding the optimal policy in the linear quadratic regulator (LQR) problem. In particular, we consider the convergence of policy gradient methods in the setting of known and unknown parameters. We are able to produce a global linear convergence guarantee for this approach in the setting of finite time horizon and stochastic state dynamics under weak assumptions. The convergence of a projected policy gradient method is also established in order to handle problems with constraints. We illustrate the performance of the algorithm with two examples. The first example is the optimal liquidation of a holding in an asset. We show results for the case where we assume a model for the underlying dynamics and where we apply the method to the data directly. The empirical evidence suggests that the policy gradient method can learn the global optimal solution for a larger class of stochastic systems containing the LQR framework and that it is more robust with respect to model mis-specification when compared to a model-based approach. The second example is an LQR system in a higher dimensional setting with synthetic data.Comment: 49 pages, 9 figure

Stochastic MPC for real-time market-based optimal power dispatch

We formulate the problem of dynamic, real-time optimal power dispatch for electric power systems consisting of conventional power generators, intermittent generators from renewable sources, energy storage systems and price-inelastic loads. The generation company managing the power system can place bids on the real-time energy market (the so-called regulating market) in order to balance its loads and/or to make profit. Prices, demands and intermittent power injections are considered to be stochastic processes and the goal is to compute power injections for the conventional power generators, charge and discharge levels for the storage units and exchanged power with the rest of the grid that minimize operating and trading costs. We propose a scenario-based stochastic model predictive control algorithm to solve the real-time market-based optimal power dispatch problem

An Integrated Framework for Modelling and Control of eP2P Interactions based on Model Predictive Control

The energy paradigm is undergoing substantial changes in recent years. In terms of production, it is observable how distributed generation, with an ever-increasing contribution from renewable sources, is displacing large concentrated generation plants. But the fundamental change is not so much about energy supply as about diluting the historical roles of producers and consumers to give way to the concept of prosumers. That is, instead of just being energy consumers, households and industries also become producers. In principle, the purpose of this production, which is inherently distributed, is self-consumption. However, when there is a surplus of production, prosumers can choose between storing the excess, if they have an energy storage system, or sell the unused fraction of energy. An obvious type of prosumers are those industries that have renewable generation facilities and which, as a consequence of their production process, generate by-products that can be used for cogeneration. In this case an obvious problem for the company is to select at all times the power sources that minimize the cost of production, which is known as Optimal Power Dispatch (OPD). If, in addition, the energy consumption time profile of the manufacturing process (per unit of raw material introduced) is known, it is also possible to make an optimal production schedule to minimize energy cost, which is called Optimal Power Scheduling (OPS). Chapter 3 presents an Economic Model Predictive Controller (EMPC) that simultaneously performs OPD and OPS using an olive mill as an example. The emergence of the role of energy prosumers makes it necessary to extend, improve or replace the traditional mechanisms of energy exchange. This thesis includes novel approaches for modelling the behaviour of prosumers. It also proposes new structures to facilitate energy trading, always from the perspective of the peerification of the energy paradigm. Thus, another line of research studies the establishment of peer-to-peer (P2P) markets for the exchange of energy between heterogeneous prosumers (homes, vehicles, intelligent buildings, etc.). The efficiency of markets based on both discrete double auctions (DDAs) and continuous double auctions (CDAs) is compared. An Energy Management System (EMS) is also introduced including market agent software that allows the necessary tasks for participation in the auctions to be carried out automatically (determination of private valuation, role selection and price adaptation). Chapter 4, Chapter 5 and Chapter 6 present some examples of such exchange markets stablished between different types of prosumers: i) energy market for electric vehicles that coincide parked in a large workplace, ii) power market for households within the same neighbourhood and iii) integrated energy and power markets for heterogeneous energy entities. The evolution of aforementioned mechanisms and the appearance of new market models must be accompanied by the development of control techniques that optimise and automate all the processes related to energy saving and trading, by a group of increasingly heterogeneous prosumers. This thesis deals with how different variants of predictive controllers can contribute to this last aspect. For industries with cogeneration capacity, the EMPC contributes to the optimal scheduling of production to maximise the return from energy reuse, either through self-consumption or through the trading of surpluses. The use of stochastic predictive control is proposed in order to maximise the expected return on the participation of prosumers, whatever their type, in continuous markets where the price of energy may undergo stochastic variations.El paradigma energético está experimentando cambios sustanciales en los últimos años. En cuanto a la producción, se observa cómo la generación distribuida, con un aporte cada vez mayor de fuentes renovables, está desplazando a las grandes plantas de generación concentrada. Pero el cambio fundamental no consiste tanto en el suministro de energía como en la dilución de la clasificación tradicional entre productores y consumidores para dar paso al concepto de prosumidores. Es decir, en lugar de ser simplemente consumidores de energía, los hogares y las industrias también se convierten en productores. En principio, el objetivo de esta producción, que es intrínsecamente distribuida, es el autoconsumo. Sin embargo, cuando hay un excedente de producción, los prosumidores pueden elegir entre almacenar el excedente, si tienen un sistema de almacenamiento de energía, o vender la fracción no utilizada de la energía. Un tipo obvio de prosumidores son aquellas industrias que cuentan con instalaciones de generación renovable y que, como consecuencia de su proceso de producción, generan subproductos que pueden ser utilizados para la cogeneración. En este caso, un problema obvio para la empresa es seleccionar en todo momento las fuentes de energía que minimizan el coste de producción, lo que se conoce como Optimal Power Dispatch (OPD). Si, además, se conoce el perfil temporal de consumo de energía asociado al proceso de fabricación (por unidad de materia prima introducida), también es posible realizar un programa de producción óptimo para minimizar el coste de la energía, lo cual se denomina Optimal Power Scheduling (OPS). El capítulo 3 presenta un Controlador Predictivo Económico basado en Modelo (EMPC) que realiza simultáneamente OPD y OPS utilizando como caso de estudio una almazara olivarera. La aparición de la figura de los prosumidores energéticos hace necesario ampliar, mejorar o sustituir los mecanismos tradicionales de intercambio energético. Esta tesis incluye enfoques novedosos para modelar el comportamiento de los prosumidores. También propone nuevas estructuras para facilitar el comercio de energía, siempre desde la perspectiva de la peerificación del paradigma energético. Así, otra línea de investigación estudia el establecimiento de mercados peer-to-peer (P2P) para el intercambio de energía entre prosumidores heterogéneos (viviendas, vehículos, edificios inteligentes, etc.). Se compara la eficiencia de los mercados basados tanto en subastas dobles discretas (Discrete Double Auction - DDA) como en subastas dobles continuas (Continuous Double Auctions - CDA). También se introduce un Sistema de Gestión Energética (Energy Management System - EMS) que incluye un software de agente de mercado que permite que las tareas necesarias para la participación en las subastas (determinación de la valoración privada, selección de roles y adaptación de precios) se lleven a cabo automáticamente. Los capítulos 4, 5 y 6 presentan algunos ejemplos de estos mercados de intercambio establecidos entre diferentes tipos de prosumidores: i) mercado de energía para vehículos eléctricos que coinciden aparcados en un gran lugar de trabajo, ii) mercado de energía para hogares dentro de un mismo barrio y iii) mercados integrados de energía y electricidad para entidades energéticas heterogéneas. La evolución de los mecanismos mencionados y la aparición de nuevos modelos de mercado deben ir acompañados del desarrollo de técnicas de control que optimicen y automaticen todos los procesos relacionados con el ahorro y la comercialización de la energía, por parte de un conjunto de prosumidores cada vez más heterogéneos. Esta tesis trata de cómo las diferentes variantes de los controladores predictivos pueden contribuir a este último aspecto. Para las industrias con capacidad de cogeneración, el EMPC contribuye a la programación óptima de la producción para maximizar el rendimiento de la reutilización de la energía, ya sea a través del autoconsumo o de la comercialización de excedentes. Por otro lado, se propone el uso del control predictivo estocástico para maximizar el rendimiento esperado de la participación de los prosumidores, cualquiera que sea su tipo, en mercados P2P donde el precio de la energía está sujeto a incertidumbres