Smart Polygeneration Microgrid Research Articles

Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.

Probabilistic Power Forecasting in a Microgrid for the Integration of Distributed Energy Resources

The integration of distributed energy resources in the power grid supposes a great challenge, considering the expected growing in the electric mobility and the renewable generation. Therefore, to support the successful deployment of these resources, energy management according to the expected behaviors of the elements of power grids is required. In this sense, the probabilistic forecasting methods are gaining acceptance, as they provide richer information than deterministic methods thanks to the consideration of expected probabilities. One of their problems is that the application of metrics for choosing the best models is not simple, depending on the objective of the type of probabilistic prediction that must be performed. In this paper, diverse types of forecasting are performed, and two possible approaches for selecting interval forecasting models are compared in a case study centered on the Smart Polygeneration Microgrid (SPM) located on the Savona Campus of University of Genova (Italy). The results show that, for the machine learning models that were tested, it is better to obtain a model for each of the different types of forecasting that are required (quantiles, and the prediction intervals) to have better-quality predictions. The average improvement on the quality of the tested prediction intervals for the three forecasted variables were a 27.40%, 7.39%, and 28.65% in their Winkler score respectively.

Sustainable district optimization with coupled power and district heating networks

In a framework where the integration of renewable energy sources has a crucial role for addressing global energy challenges, optimal operations of the heating must be correctly addressed. This paper addresses the optimization of integrated electric and thermal networks in the context of smart grids with District Heating Networks (DHNs). It presents an Energy Management System (EMS) for efficient management, considering thermal models for buildings. Through a real case study at the Savona Campus, encompassing a smart polygeneration microgrid and DHN, the proposed model ensures temperature regulation while minimizing costs. Results demonstrate precise temperature control, particularly in highly responsive buildings like the Savona Campus.

Smart Grid Stochastic Optimization with Ant Colony-based Scenario Generation

This paper presents a stochastic optimization approach for the operational management of sustainable energy districts and polygeneration microgrids. Stochastic operation optimization allows for an uncertain approach to deal with imprecise variables. A new approach is here presented for generating scenarios based on Ant Colony Optimization (ACO) to assess the uncertainties related to inexact data, renewable energy sources and power demand. In fact, due to the uncertainties related to forecasting loads and renewables, it is necessary to analyze the probability of occurrence of the prediction and the different scenarios that could be faced. Then, based on the generated scenarios and probabilities, a scenario-based two-stage stochastic optimization approach has been formulated to optimize the operation strategies of the various technologies and solve the unit commitment problem under uncertainty. The developed models have been applied to the Savona Campus Smart Polygeneration Microgrid, and historical data from 2018 have been used.

Optimal scheduling and real-time control of a microgrid with an electrolyzer and a fuel cell systems using a reference governor approach

This paper proposes a novel approach for the optimal scheduling and control of a microgrid with an electrolyzer and fuel cell systems, both of proton exchange membrane (PEM) technology. It is based on a hierarchical procedure constituted of two levels of optimization: a higher level based on an economic optimization for the optimal scheduling of different components of the microgrid, and a lower level for the real-time control of the hydrogen systems: PEM electrolyzer (PEMEZ) and PEM fuel cell (PEMFC). The flexible operation imposed by the higher level leads to a violation of the limits designed by the manufacturer of the hydrogen components, specifically when switching from one power level to another. The current proportional-integral (PI) controllers integrated into those systems cannot handle this issue, which provokes a premature aging phenomenon of the materials and leads to poor performance of the systems. In the present paper, at the lower level, a reference governor (RG) real-time control approach has been added on top of the PI controller to ensure the respect of the operating limits and guarantee better performances. The focus has been given to the stack's temperature in both the electrolyzer and fuel cell systems as the control objective because of its direct influence on the material's durability and, by extension, on the efficiency. The bi-level optimization and control architecture has been applied and validated through simulations using data from a real-world case study, specifically the Savona Campus Smart Polygeneration Microgrid in Italy. The results showed a significant reduction in the overshoots of the stack's temperature compared to the PI controller.

A platform for demand response and intentional islanding in distribution grids: The LIVING GRID demonstration project

An optimization-based platform is presented to request ancillary services and perform demand response. The work has seen a joint effort of the Italian TSO and DSO (Transmission and Distribution System Operators) and active local prosumers within the LIVING GRID innovation and demonstration project. The platform, a pure software architecture based on existing control and supervision equipment in the prosumer's plant, embeds optimization models, can communicate reference values for active and reactive power and perform intentional islanding and demand response in portions of the distribution grid and microgrids. Two possible configurations have been tested: Single PoC (Point of Connection) and Multi-PoC. The results of the optimization model and in-field experiments are presented and discussed for the considered pilot site: the Savona Campus Smart Polygeneration Microgrid (University of Genova, Italy).

Distributed operational management of microgrids: a second order dual update approach

This paper develops a Distributed Energy Management System (EMS) to optimally allocate electric and thermal power production in a polygenerative microgrid. The EMS problem is formulated as a multiperiod convex optimization problem and solved using AL-SODU, a new distributed algorithm based on the augmented Lagrangian method with second order dual updates. The proposed AL-SODU algorithm significantly outperforms state of the art algorithms employing first order dual updates, with 15-times speedup in convergence speed. A case study of the EMS based on AL-SODU is conducted on the Smart Polygeneration Microgrid (SPM) located on the Savona Campus of University of Genova (Italy). The EMS determines the optimal schedules for electric and thermal power plants every 15-minutes. This real time scheduling of the microgrid is enabled by the Al-SODU algorithm, which solves the scheduling problem in 1.86s.

Distributed control for polygeneration microgrids: A Dynamic Market Mechanism approach

This paper develops a Dynamic Market Mechanism (μDMM) to optimally allocate electric and thermal power in a combined heat and power microgrid. The market mechanism is formulated as a receding horizon constrained optimization problem, from which an optimal automated transactive procedure is developed. Its operation is distributed in nature and incorporates the most up-to-date electric and thermal load estimates and renewable generation. The results of the market clearing of the proposed microgrid μDMM are the spot prices and schedules for electric and thermal power in addition to non-binding estimates of future prices and set-points. The performance of the μDMM is illustrated using simulation studies of a Combine Heat and Power (CHP) microgrid model based on the Smart Polygeneration Microgrid (SPM) located on the University of Genova Savona Campus. The results show that the μDMM has the ability to adapt to new information via updated forecasts and improve the performance of microgrid operation compared to a centralized method. This in turn makes the μDMM attractive for use in μEMS (microgrid Energy Management Systems) by allowing new energy resources to be added in a plug and play fashion.

Simulation and design of a large thermal storage system: Real data analysis of a smart polygeneration micro grid system

• Optimization of the size of a smart grid integrated thermal storage system. • Implementation of a lumped parameter model based on historical measured data series. • Maximization of the overnight energy production through sensible heat thermal storage. • Investment profitability in terms of CO 2 reduction, share of energy from cogeneration, economics. The Smart Polygeneration Microgrid (SPM) at the Savona Campus of the University of Genoa consists of several renewable and traditional electrical and thermal generating units integrated with electric batteries; buildings and facilities are connected by an electrical grid and a district heating network. Thermal and electrical power flows are managed by a proper Energy Management System (EMS) which includes models for all the SPM components ensuring operational costs minimization in compliance with networks constraints. The present study focuses on the integration of a thermal energy storage (TS) system in the SPM: to this aim, a new thermal storage model based on a stepped two-zone approach has been built inside the dynamic EMS solver. The TS is conceived for recovering additional heat from two 65kW el cogenerative gas turbines (CGT), thus minimizing the operation time of the backup gas heaters (GH): the analysis is aimed at inferring the best TS size in terms of different key parameters, expressed through properly defined economic and energy criteria, including overall energy consumption, reduced greenhouse emissions, minimum payback period. The model is applied to energy demand data (heat and electricity) as measured at 15 min time steps along 2 years. The present dynamic analysis shows that an optimized size TS based on sensible heat can increase the CGT operation hours up to 34% yearly and reduce the equivalent CO 2 emissions by 4%.

HArMoNICS: High-Assurance Microgrid Network Infrastructure Case Study

Modern Intelligent Infrastructures (II) are highly complex, interconnected systems that are now emerging. For instance, II can integrate technologies and processes to provide citizens with faster services and better goods. An average II can include many technologies, e.g., Cloud applications and IoT devices, under different environments, e.g., industry 4.0 production plants and smart buildings. Although II bring concrete benefits to all of these contexts, they also carry security concerns. Reasoning about threats and security exposures that might affect II is non trivial. This is only partially due to their inherent complexity. As a matter of fact, real II are typically in charge of some critical operations that cannot be interrupted or compromised for experimental purposes. An alternative solution is to rely on digital replicas which can provide a good trade off between realism and usability. These assets represent a strategic and highly demanded resource for the security community. In this paper we present HArMoNICS, a case study infrastructure meant to provide a playground for security experts interested in II security. HArMoNICS revolves around a digital replica of a real Smart Polygeneration Microgrid (SPM) located in Italy. Although most of the components are based on or inspired to the real system, HArMoNICS has been enriched with further security-relevant features. As a result, the case study includes vertical uses cases focusing on specific security topics. Security researchers can use it to assess the effectiveness of new methodologies, to carry out security training activities, or even to extend it with new elements.

An Energy Management Platform for the Optimal Control of Active and Reactive Powers in Sustainable Microgrids

This paper presents an energy management platform based on a receding-horizon scheme for the optimal control of active and reactive power flows in microgrids, including small-size photovoltaic, combined heat and power, wind generation, mini-hydro, and energy storage. The objective function to be minimized can be set both on the daily operational costs (economic indicator) and on the global CO2 emissions of the system (environmental indicator), whereas decision variables are the production schedules of the generators and the power flows across the grid. The tool includes the technical constraints characterizing low voltage/medium voltage (LV/MV) microgrids and gives the user the possibility to select different models for the electrical network (nonlinear power flow equations, linear approximation, and single bus-bar) and different optimization ranges. The energy management system has been validated through an experimental campaign on the smart polygeneration microgrid of the University of Genoa, which provides electricity and thermal energy to the Savona Campus, an “open-air” demo-site of an environmentally sustainable urban district with a population of about 2200 people. The results of the tests on the field highlight the robustness of the developed platform and the capability of the receding-horizon algorithm at the core of it of successfully treating data uncertainties.

Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids

In this paper, we deal with the problem of nowcasting and forecasting the photovoltaic power production (PvPP) on the basis of real data available for the Savona Campus and coming from the energy management systems (EMSs) of the smart polygeneration microgrid that feeds buildings in the University area. In this paper, we show how PvPP nowcast and forecast problems can be solved with the state-of-the-art data-driven techniques, which use the historical data collected by the EMS. In particular, we compare the performance of the kernelized regularized least squares, the extreme learning machines, and the random forests. In the machine learning field, these algorithms are the best choice in three different families of techniques: kernel methods, neural networks, and ensemble methods. Results show that our proposal can improve of almost one order of magnitude to the actual prediction system used in the EMS of the Savona Campus, which is based on the knowledge of the physical problem. Finally, by using the EMS installed at the Savona Campus, it has been possible to quantify the saving in costs and CO $_2$ emissions due to the new nowcasting and forecasting models.

Electrical storage systems based on Sodium/Nickel chloride batteries: A mathematical model for the cell electrical parameter evaluation validated on a real smart microgrid application

In the smart grid field, renewable power plants coupled with electrical storage systems are becoming a promising challenge to optimize the exploitation of renewable sources in order to maximize self-consumption, thus minimizing the exchange with the grid. Sodium/Nickel chloride batteries are considered optimal storage systems, due to their limited environmental impact, high reliability and specific energy, and reduced maintenance. The present paper describes a mathematical model elaborated to calculate the electrical parameters, during the discharge phase, of a Sodium/Nickel chloride galvanic cell contained within an electrical storage system designed for smart grid applications. Some results of the model have been compared with values achieved in experimental tests carried out on the FZSoNick electrical storage batteries of the Genoa University Smart Polygeneration Microgrid.

An architecture for the optimal control of tertiary and secondary levels in small-size islanded microgrids

Islanded microgrids are assuming a key role for the support in the distribution grid’s management, which is more and more difficult due to the massive increase of distributed generation and energy production plants from renewable resources. In this paper, a new architecture for the optimal control of tertiary and secondary levels in small-size islanded microgrids is proposed. Specifically, at each optimization step, by using a receding horizon approach, the tertiary control provides the optimal power schedule for the microgrid on the basis of economic and environmental criteria. Then, the secondary control, to provide set points for primary control, uses another objective function that minimizes the quadratic deviation from the reference values provided by the tertiary level and the desired frequency for good performances. The approach is applied to a real case study (a portion of the Savona Campus Smart Polygeneration Microgrid) characterized by a diesel engine connected to a synchronous generator, a photovoltaic plant, and an electrical storage system.

An Energy Management System for the Savona Campus Smart Polygeneration Microgrid

The paper proposes an Energy Management System (EMS) for the optimal operation of the Savona Campus Smart Polygeneration Microgrid (SPM) to minimize the overall production costs while satisfying all the thermal and electric network constraints. To do this, first an adequate model of all the components installed in the SPM is presented, then a detailed and computationally efficient electric network representation is derived. The experimental validation performed on the SPM shows a good agreement with the energy management already present in the control room of the SPM and highlights the advantages of the proposed approach both in terms of CPU effort and of completeness of the model.

Feasibility study, design and implementation of smart polygeneration microgrid at AMU

To confront the challenges associated with the rising of carbon footprint and energy demand, the Renewable Energy Sources (RESs) based smart microgrids are becoming the viable paradigm. In this work, based on cost exploration, greenhouse gas (GHG) emission, and availability of RESs, the feasibility study, design, optimization, and implementation of institutional based smart Polygeneration microgrid is presented. To investigate the most optimal scenario, the detailed analysis of various microgrid topologies in grid connected as well as in off-grid mode with and without the storage system is carried out. The analysis outcome reveals that the solar PV system in grid connected (i.e. G+PV) mode is most preferable. But, the consideration of intermittency as well as the volatile behaviour of the solar PV system forces incorporation of a battery storage system to overcome the fluctuated voltage profile and interrupted power supply, which raises the Cost of Energy (COE), Net Present Cost (NPC), and operating cost to another extent. To obtain the more realistic results, the uncertainty of load demand, as well as RESs, is also taken into account. Further, the comparative analysis of the proposed microgrid topology with existing conventional system is also presented.The result shows that in grid-connected mode, the key cost parameter belongs to PV system (i.e. INR 20.68 million) followed by grid operating cost (i.e. INR7.28 million), battery (i.e. INR 3.11 million) and converter (i.e. INR 2.083 million). While in the off-grid mode the major part of the total system cost is covered by battery storage system (i.e. INR 74.76 million) followed by PV system (i.e. INR 27.96 million) and converter (i.e. 2.385 million). The comparative study shows total system cost associated with proposed microgrid G+PV+B (i.e. INR 33.163 million) is 31.57% of current scenario while the total system cost associated with PV+B (INR 105.036 million) is 95.41% higher than the current scenario. The recommended system mitigated 226.26-kilokg of CO2, 980.93kg of SO2, and 497.73kg of Nitrogen oxides per year.

A Simplified Microgrid Model for the Validation of Islanded Control Logics

Microgrids (MGs) may represent a solution in the near future to many problems in the energy and electric world scenarios; such as pollution, high reliability, efficiency and so on. In particular, MGs’ capability to work in an islanded configuration represents one of their most interesting features in terms of the improvement of the reliability of the system, the integration of renewable energy sources and the exploitation of the quick response and flexibility of power electronic devices in a stand-alone system. In order to study and validate innovative solutions and control strategies for islanded operation, there is a need to develop models for MG structures that can be reliable and sufficiently simple to be used for the purpose of the design and validation of innovative control systems. This paper proposes a simplified, first harmonic model for a generic structure of MG characterized by its use of only electronic power converter interfaced generation. The main advantages of the proposed method lie in the model’s simplicity and its reduced solving time, thanks to the limited number of necessary parameters to describe the system. Moreover, the developed formulation allows the avoidance of specific (and often licensed) software to simulate the system. The performances of the proposed model have been validated by means of a comparative analysis of the results obtained against a more accurate representation of the system performed in the power system CAD—electromagnetic transient and DC (PSCAD—EMTDC) environment, which allows for the representation of each component with a very high level of detail. Such comparison has been performed using the University of Genoa Savona Campus Smart Polygeneration Microgrid testbed facility, due to the availability of all the necessary numerical values.

A Dynamic Market Mechanism for Combined Heat and Power Microgrid Energy Management

This paper develops a dynamic market mechanism (DMM) to optimally allocate electric and thermal power in a combined heat and power microgrid. The market is formulated as a receding horizon constrained optimization problem, from which an optimal automated transactive procedure is developed. The market operation is distributed in nature and incorporates the most up-to-date electric and thermal load estimates and renewable generation. These properties make our microgrid DMM, µDMM, attractive for microgrid energy management systems, as new smart buildings, battery storage systems, and renewable energy resources can be added in a plug-and-play fashion without reformulating the optimization problem and without adding computational complexity to the EMS. The result of the market clearing is the spot prices and set-points for electric and thermal power in addition to non-binding estimates of future prices and set-points. The the market mechanism is simulated on a CHP Microgrid model based on the Smart Polygeneration Microgrid (SPM) located on the University of Genoa’s Savona campus.

Towards innovative district energy management: a case study with stochastic renewable generators

The conversion of ocean wave power into sustainable electrical power represents a major opportunity to Nations endowed with such a kind of resource. At the present time the most of the technological innovations aiming at converting such resources are at early stage of development, with only a handful of devices close to be at the commercial demonstration stage. The Seaspoon device, thought as a large energy harvester, catches the kinetic energy of ocean waves with promising conversion efficiency, and robust technology, according to specific “wave-motion climate†. University of Genoa developed and patented a prototype to be deployed in medium average energy content seas (i.e. Mediterranean or Eastern Asia seas). This paper presents the installation phases of the first real scale prototype installed in the gulf of Genova and the monitoring of its performances. A brief description of the Seaspoon WEC is presented together with the monitoring equipment and procedures.In this research a thermoeconomic analysis of its integration in a real polygenerative district is also investigated. The impact of such this kind of stochastic renewable generator in the Savona Campus Smart Polygeneration Microgrid (SPM) is evaluated. The SPM plant is made up by (i) two auxiliary boilers (500kWth each), (ii) four micro gas turbines (30kWe, 2x65kWe and 100kWe), (iii) an internal combustion engine fed by natural gas (20kWe), (iv) an absorption chiller (100 kWf) and (v) PV panels for a total power installed of 100 kWe. Generators are “distributed†around the campus and they are coupled to electrical and thermal storages. Since the system is constituted by co-generative prime movers it can supply both electrical and thermal energy of the campus and the integration of storage is really important in order to follow both the requests, pursuing the best management strategy. The analysis of this smart-grid is performed exploiting a software developed by the Author's research group, which allows for the thermo-economic optimization of poly-generative energy systems. A model of the real plant was built and it was implemented in the software. The off-design curves of the real devices installed in the campus were used in order to increase the reliability of the simulation results. The grid was simulated considering the time dependent nature of the demands throughout the whole year. The model was used to simulate the smart grid behavior during the whole year, and find the best operational strategy. A time-dependent thermo-economic hierarchical approach has been used, considering the time-dependent electrical, thermal and cooling load demands during the year as problem constraints. The results are presented and discussed in depth and show the strong interaction between fossil and renewable resources, particularly the impact of unpredictable and randomized generators like the WECs ones. A dedicated model of the Seaspoon was implemented and exploited in the code.

Definition and on-field validation of a microgrid energy management system to manage load and renewables uncertainties and system operator requirements

The present paper proposes an Energy Management System (EMS) to be used in grid connected microgrids. To do this, first a suitable model (for optimization purposes) of all the components that typically appear in a microgrid is presented, then, four possible electric network models are detailed and finally the overall architecture of the optimization problem of the EMS is set up. Moreover, as the optimization of the energy production/consumption of a microgrid relies on the thermal and electric load and on the renewables forecasting, an online empirical correction of forecasted data is proposed, highlighting its positive impact on the overall operational cost of the microgrid. Another aspect which is accounted regards the possibility of allowing the EMS to act as a power plant controller in compliance with the Distribution System Operator (DSO) requirements in terms of reactive power management and voltage control (as requested by the majority of grid codes and national regulations). So, the proposed algorithm structure splits the optimization problem into two sub problems: the first one basically dictates the active power production of the dispatchable units minimizing an economic objective function, while the second accounts for the satisfaction of the DSO requirements. The experimental validation of the proposed EMS is performed on the University of Genoa Smart Polygeneration Microgrid (SPM), where the proposed EMS is currently running. The considered test cases highlight the effectiveness of the proposed EMS in economically operating the microgrid (compared to simpler EMS previously installed in the SPM) and in satisfying the reactive power or voltage regulation requirements provided by the Italian technical requirements.