Joint Optimal Pricing and Electrical Efficiency Enforcement for Rational Agents in Micro Grids

In electrical distribution grids, the constantly increasing number of power generation devices based on renewables demands a transition from a centralized to a distributed generation paradigm. In fact, power injection from Distributed Energy Resources (DERs) can be selectively controlled to achieve other objectives beyond supporting loads, such as the minimization of the power losses along the distribution lines and the subsequent increase of the grid hosting capacity. However, these technical achievements are only possible if alongside electrical optimization schemes, a suitable market model is set up to promote cooperation from the end users. In contrast with the existing literature, where energy trading and electrical optimization of the grid are often treated separately or the trading strategy is tailored to a specific electrical optimization objective, in this work we consider their joint optimization. Specifically, we present a multi-objective optimization problem accounting for energy trading, where: 1) DERs try to maximize their profit, resulting from selling their surplus energy, 2) the loads try to minimize their expense, and 3) the main power supplier aims at maximizing the electrical grid efficiency through a suitable discount policy. This optimization problem is proved to be non convex, and an equivalent convex formulation is derived. Centralized solutions are discussed first, and are subsequently distributed. Numerical results to demonstrate the effectiveness of the so obtained optimal policies are then presented

Smart Energy Grids and Complexity Science

This report proposes ideas and an approach to address present and future challenges in future smart energy systems through the particular lenses of complexity sciences. Complexities arising inside and around emerging energy distribution systems prompt a multilayered and integrated approach in which different disciplines and areas of expertize are pooled together. The interfaces between system layers and intellectual disciplines are the focus, rather than on the details of any individual layer or the particularities of one approach. A group of people sharing this view and willing to procede in this way organized a workshop at the Joint Research Centre of the European Commission, Petten, the Netherlands on 24th June 2012.Experts from different field of expertise convened to present their current research and discuss the future challenges of emerging smart energy systems via the afore-mentioned perspectives.JRC.F.3-Energy securit

Smart Grids and Complexity Science

Postprint (published version

MODEL PREDICTIVE CONTROL OF BUILDING ENERGY MANAGEMENT SYSTEMS IN A SMART GRID ENVIRONMENT

Buildings are a major source of energy consumption. In the United States, buildings are responsible for more than 70% of all power consumption. Over 40% of this building power consumption is from the Heating, Ventilation, and Air Conditioning (HVAC) systems. Modern technologies such as building Energy Storage Systems (ESS), renewable energy sources, and advanced control algorithms allow for so-called Smart Buildings to increase energy efficiency. Smart Buildings further benefit from existing in a Smart Grid environment, where information such as pricing and anticipated power load is sent over two way communitcation between the grid operator and the power consumer. The traditional control systems for these HVAC systems are often simple and do not exploit the principles of optimal control. This study applies Model Predictive Control (MPC) and ESS to the problem of controlling a Smart Building in a Smart Grid environment. Simulations are performed for various optimal control objective functions. These objectives include price minimization, energy minimization, and an introduced Building to Grid (B2G) index optimization. The B2G optimization aims to both decrease the price of power for the consumer while avoiding large spikes in power consumption to maintain a steady load profile which benefits the grid operator. The results show that MPC has potential for large performance increases in Building Energy Management, while meeting the constraints for B2G integration

Model-driven development of data intensive applications over cloud resources

The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and low-latency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array