Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage

Optimal energy management of a campus microgrid considering financial and economic analysis with demand response strategies

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts

Towards transactive energy systems: An analysis on current trends

This paper presents a comprehensive analysis on the latest advances in transactive energy systems. The main contribution of this work is centered on the definition of transactive energy concepts and how such systems can be implemented in the smart grid paradigm. The analyzed works have been categorized into three lines of research: (i) transactive network management; (ii) transactive control; and (iii) peer-to-peer markets. It has been found that most of the current approaches for transactive energy are available as a model, lacking the real implementation to have a complete validation. For that purpose, both scientific and practical aspects of transactive energy should be studied in parallel, implementing adequate simulation platforms and tools to scrutiny the results.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No. 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.info:eu-repo/semantics/publishedVersio

The implementation of energy sharing using a system of systems approach

There is an increasing demand for renewable energy and consumers need more procurement options to meet their needs. Energy sharing provides a peer-to-peer (P2P) marketplace where prosumer electricity is redistributed to fellow energy-sharing community participants. This redistribution of prosumer electricity provides consumers with additional electricity suppliers, while also decreasing the load on the utility company. Though significant progress has been made regarding research and implementation of energy sharing, there is still room for growth when evaluating energy-sharing communities and defining appropriate community coordination based on end-user needs. The first contribution in this work identified nine characteristics of energy-sharing communities as a decentralized complex adaptive system of systems (DCASoS). Considering each characteristic before determining community coordination is vital to ensure ample participation within the energy-sharing community. The second contribution was the exploration of a two-stage stochastic programming model as an alternative to the classic energy distribution business model. The third contribution compares three behavioral theories to identify the best fitting model to predict interest in participating in an energysharing community. This research provides companies with foundational knowledge to develop an energy-sharing community that both fulfills end-user satisfaction and increases robustness of electricity distribution business models --Abstract, page iv

Stochastic Operation Scheduling Model for a Swedish Prosumer with PV and BESS in Nordic Day-Ahead Electricity Market

In this paper, an optimal stochastic operation\ua0scheduling model is proposed for a prosumer owning\ua0photovoltaic (PV) facility coupled with a Battery Energy\ua0Storage System (BESS). The objective of the model is to\ua0maximize the prosumer’s expected profits. A two-stage\ua0stochastic mixed-integer nonlinear optimization (SMINLP)\ua0approach is used to cope with the parameters’ uncertainties.\ua0Artificial Neural Networks (ANN) are used to forecast themarkets’ prices and the standard scenario reduction\ua0algorithms are applied to handle the computational\ua0tractability of the problem. The model is applied to a case\ua0study using data from the Nordic electricity markets and\ua0historical PV production data from the Chalmers University\ua0of Technology campus, considering a scaled up 5MWp power\ua0capacity. The results show that the proposed approach could\ua0increase the revenue for the prosumer by up to 11.6% as\ua0compared to the case without any strategy. Furthermore, the\ua0sensitivity analysis of BESS’s size on the expected profit shows\ua0that increasing BESS size could lead to an increase in the net\ua0profits

Dynamic Pricing for Microgrids Energy Transaction in Blockchain-based Ecosystem

Microgrid (MG) is an efficient platform to integrate distributed energy resources in distribution networks. The operation of MG is also expected to take advantage of emerging smart grid technologies to improve operation and robustness. Among these emerging technologies, blockchain technology provide a big potential to rule the energy transaction in an innovative way. In this paper, a physical architecture of the ecosystem with MGs is firstly presented. Moreover, as the main parts of the blockchain technology, the operation of distributed ledger and smart contracts are introduced in the transaction process. Considering dynamic pricing scheme in the process of energy transaction in the ecosystem, we model the energy transaction between MGs and distribution system operator (DSO) to decide the trading amount and price of the energy. The welfare maximization mathematical model is established accordingly, and the formulated dual problem will be used to find the shadow price of selling renewable energy to grid and real-time retailer price from DSO. Finally, with the deployment of distribution ledger, the energy transaction process can be fully recorded, and transaction execution can be achieved with the help of smart contracts. In light of the mentioned perspective, beside demonstrated benefit brought to both MGs and DSO, the energy transaction and management based on the blockchain will result in higher reliability and improved auditability in the ecosystem

An Aggregation Model for Energy Resources Management and Market Negotiations

Currently the use of distributed energy resources, especially renewable generation, and demand response programs are widely discussed in scientific contexts, since they are a reality in nowadays electricity markets and distribution networks. In order to benefit from these concepts, an efficient energy management system is needed to prevent energy wasting and increase profits. In this paper, an optimization based aggregation model is presented for distributed energy resources and demand response program management. This aggregation model allows different types of customers to participate in electricity market through several tariffs based demand response programs. The optimization algorithm is a mixed-integer linear problem, which focuses on minimizing operational costs of the aggregator. Moreover, the aggregation process has been done via K-Means clustering algorithm, which obtains the aggregated costs and energy of resources for remuneration. By this way, the aggregator is aware of energy available and minimum selling price in order to participate in the market with profit. A realistic low voltage distribution network has been proposed as a case study in order to test and validate the proposed methodology. This distribution network consists of 25 distributed generation units, including photovoltaic, wind and biomass generation, and 20 consumers, including residential, commercial, and industrial buildings.This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO). This work also received funding from the following projects: NETEFFICITY Project (ANI | P2020 – 18015); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio