Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Nowadays, integration of more renewable energy resources into distribution systems to inject more clean en- ergy introduces new challenges to power system planning and operation. The intermittent behaviour of variable renewbale resources such as wind and PV generation would make the energy balancing more difficult, as current forecasting tools and existing storage units are insufficient. Transmission system operators may withstand some level of power imbalance, but fluctuations and noise of profiles are undesired. This requires local management performed or encouraged by distribution system operators. They could try to involve aggregators to exploit flexibility of loads through demand response schemes. In this paper, we present an optimal power flow-based algorithm written in Python which reads flexibility of different loads offered by the aggregators from one side, and the power flow deviation with respect to the scheduled profile at transmission-distribution coupling point from the other side, to define where and how much load to adjust. To demonstrate the applicability of this core, we set-up a real- time simulation-based test bed and realised the performance of this approach in a real-like environment using real data of a network

GIS-based Software Infrastructure to Model PV Generation in Fine-grained Spatio-temporal Domain

Nowadays, we are moving forward to more sustainable societies, where a crucial issue consists on reducing footprint and greenhouse emissions. This transition can be achieved by increasing the penetration of distributed renewable energy sources together with a smarter use of energy. To achieve it, new tools are needed to plan the deployment of such renewable systems by modelling variability and uncertainty of their generation profiles. In this paper, we present a distributed software infrastructure for modelling and simulating energy production of Photovoltaic (PV) systems in urban context. In its core, it performs simulations in a spatio-temporal domain exploiting Geographic Information Systems together with meteorological data to estimate Photovoltaic generation profiles in real operating conditions. This solution provides results in real-sky conditions with different time-intervals: i) yearly, ii) monthly and iii) sub-hourly. To evaluate the accuracy of our simulations, we tested the proposed software infrastructure in a real world case study. Finally, experimental results are presented and compared with real energy production data collected from PV systems deployed in the case study area

Distributed Infrastructure for Multi-Energy-Systems Modelling and Co-simulation in Urban Districts

In recent years, many governments are promoting a widespread deployment of Renewable Energy Sources (RES) together with an optimization of energy consumption. The main purpose consists on decarbonizing the energy production and reducing the CO2 footprints. However, RES imply uncertain energy production. To foster this transition, we need novel tools to model and simulate Multi-Energy-Systems combining together different technologies and analysing heterogeneous information, often in (near-) real-time. In this paper, first we present the main challenges identified after a literature review and the motivation that drove this research in developing MESsi. Then, we propose MESsi, a novel distributed infrastructure for modelling and cosimulating Multi-Energy-Systems. This infrastructure is a framework suitable for general purpose energy simulations in cities. Finally, we introduce possible simulation scenarios that have different spatio-temporal resolutions. Space resolution ranges from the single dwelling up to districts and cities. Whilst, time resolution ranges from microseconds, to simulate the operational status of distribution networks, up to years, for planning and refurbishment activities

Realistic Multi-Scale Modelling of Household Electricity Behaviours

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of information from Census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a Bottom-up approach based on Monte Carlo Non Homogeneous Semi-Markov, we provide household end-user behaviours and realistic households load profiles on a daily as well as on a weekly basis, for either weekdays and weekends. The proposed approach overcomes limitations of state-of-art solutions that do not consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration, or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited on a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained simulating realistic populations in a period covering a whole calendar year and analyse our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at household, national and European levels, respectively

An online reinforcement learning approach for HVAC control

Heating, Ventilation and Air Conditioning (HVAC) optimization for energy consumption reduction is becoming ever more a topic of the utmost environmental and energetic concerns. The two most employed methodologies for optimizing HVAC systems are Model Predictive Control (MPC) and Reinforcement Learning (RL). This paper compares three different RL approaches to HVAC optimization: one based on a black-box system identification model trained on historical data, one based on a white-box model of a building and one online method based on an imitation learning pretraining phase on historical data. The three approaches are compared with a literature baseline and an EnergyPlus baseline. Results show that the overall best method in terms of energy consumption reduction (65% decrease) and thermal comfort increase (25% increase) is the approach based on the white-box model. However, the proposed methodology, based on online and imitation learning, demonstrates remarkable efficiency, achieving comparable improvements in energy consumption after just a few months of online training, while maintaining thermal comfort at around the same level as the baseline. These results prove a direct online RL approach, which avoid the use of costly simulations, can provide a reliable and inexpensive solution to the problem of HVAC optimization

A hierarchical and modular agent-oriented framework for power systems co-simulations

AbstractDuring the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid "smart". Often, these domain-specific simulators have been then coupled with co-simulation platforms to test new scenarios. In parallel, agent-oriented approaches have been introduced to test distributed control strategies and include social and behavioural aspects typical of the consumer side. Rarely, simulators of the physical systems have been coupled with these innovative techniques, especially when social and psychological aspects have been considered. In order to ease the re-usability of these simulators, avoiding re-coding everything from scratch, we propose a hierarchical and modular agent-oriented framework to test new residential strategies in the energy context. If needed, the presented work enables the user to select the desired level of details of the agent-based framework to match the corresponding physical system without effort to test very different scenarios. Moreover, it allows adding on top of the physical data, behavioural aspects. To this end, the characteristics of the framework are first introduced and then different scenarios are described to demonstrate the flexibility of the proposed work: (i) a first stand-alone scenario with two hierarchy levels, (ii) a second co-simulation scenario with a photovoltaic panel simulator and (iii) a third stand-alone scenario with three hierarchy levels. Results demonstrate the flexibility and ease of use of the framework, allowing us to compare several scenarios and couple new simulators to build a more and more complex environment. The framework is in the early stages of its development. However, thanks to its properties in the future it could be extended to include new actors, such as industries, to get the full picture

A User-Centric View of a Demand Side Management Program: From Surveys to Simulation and Analysis

Residential Demand Side Management (DSM) strategies increase the efficiency of the smart grid. However, the efficacy of these strategies relies on the participation of customers in DSM programs, an issue usually neglected in the analysis. To encompass all aspects, we tried to identify what are the drivers for the user engagement, focusing on the social and psychological behaviour of the user in order to simulate and analyse a residential DSM program with a centralised approach. In particular, the DSM program minimises costs taking into account different energy sources and performing load shifting considering and learning users’ acceptance of requests. The results show the advantage of a preferences-aware approach, highlighting the importance of user satisfaction on participation

A win-win algorithm for aggregated residential energy management: resource optimisation and user acceptance learning

This paper proposes a solution based on Multi Agent System to study a residential Demand Side Management (DSM) program with a centralised approach. It focuses on minimising the cost considering different energy sources, such as photovoltaic panels and energy storage system, while optimally scheduling the appliances that can be shifted in time. The cost minimisation is formulated as a Mixed Integer Linear Programming (MILP) problem. The optimal allocation of the shiftable loads takes into account the modelled users’ preferences that are learnt by means of an algorithm based on an explore-exploit strategy. From the results, it emerges that a win-win situation could be achieved if user preference are considered.These benefits include savings and users’ satisfaction

A Compact PV Panel Model for Cyber-Physical Systems in Smart Cities

One of the ambitious goals of the ‘‘Smart city’’ paradigm is to design zero-energy buildings. Buildings can be considered as connected cyber-physical systems that require the construction of sound methodologies inherited from the Electronic Design Automation (EDA) research. In particular, aiming at autonomous buildings, the effective design of renewable energy sources is a key aspect for which such methodologies have to be developed. In this work, we propose a modeling strategy for the early estimation of the performance of photovoltaic (PV) arrays. Although a plethora of PV panel models there exists, most of these models suffer from accuracy/complexity tradeoffs. On one hand, building fast models forces to ignore either the correlation between temperature and irradiance, or the topology of panels, thus yielding inaccurate estimations. On the other, more accurate models are time consuming and require costly measurements or circuit analysis, that cannot be extracted from the sole datasheet. This paper proposes a compact semi-empirical model, suitable for real time simulation and built solely from information derived from the PV panel datasheet. The model is built by empirically fitting an expression of the panel operating point as a function of both irradiance and temperature, and of the adopted PV system topology. The accuracy and effectiveness of the proposed model have been validated w.r.t. the production traces of the PV systems of a real world industrial building

A Novel Integrated Real-time Simulation Platform for Assessing Photovoltaic Penetration Impacts in Smart Grids

For future planning and development of smart grids, it is important to evaluate the impacts of PV distributed generation, especially in densely populated urban areas. In this paper we present an integrated platform, constituted by two main components: a PV simulator and a real-time distribution network simulator. The first simulates real-sky solar radiation of rooftops and estimates the PV energy production; the second simulates the behaviour of the network when generation and consumption are provided at the different buses. The platform is tested on a case study based on real data for a district of the city of Turin, Italy