Predictive Analytics for Enterprise Innovation of Retail Energy Market Modeling of Integrated Demand Response and Energy Hubs
Systems
Abstract and Figures
Many combined heat and power (CHP) energy hubs work within the following heat load mode in the wintertime to supply the request for heat, and renewable energy has been often restricted in the unified energy network (UEN) markets. The power Internet of Things (PIoTs) has enabled UEN to transmit data increasingly frequently. As a result of flexible connections among various UEN networks, renewable energy increases its accommodation capacity considerably. Thus, the purpose of the study is to optimize UEN within the backdrop of PIoTs. According to the impact of PIoTs on UEN, this paper develops the combined demand response (DR) process and the layout of the important parts of UEN. Afterward, this study develops a bi-level economic dispatching process based on the cyber-physical systems of PIoTs and UEN. In the dispatching process, the higher level optimizes the total UEN function; the lower level optimizes the demand-side equipment output and combined DR. Then, the gray wolf optimization scheme is used to solve the bi-level dispatch. Lastly, the standard UEN and the practical network have been used to verify the efficiency of the suggested process.
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... • Optimized Data Transmission and Communication: To reduce the amount of energy used during data transmission, use energy-efficient routing algorithms and communication protocols [20]. • Predictive Maintenance: Reduce energy waste from system failures by using predictive maintenance solutions that use sensor data to monitor equipment status and execute repairs proactively [21]. • Energy-Neutral Operation: Try to achieve self-sustaining or energy-neutral operation, where the energy produced from renewable sources is equal to or greater than the energy that the WSN nodes use [22]. ...
The main objective of this paper is to review the importance of renewable energy based microgrid on wireless sensor networks (WSNs) including challenges of renewable energy-based MGs. This paper explores the critical nexus between Wireless Sensor Networks (WSNs) and microgrids powered by renewable energy, explaining the rising importance of these networks and the issues they provide. Microgrids have become a promising technology in the worldwide search for sustainable energy solutions, since they allow for the effective integration of distributed energy management, local energy production, and Renewable Energy (RE) sources. WSNs allow for the microgrid-wide real-time monitoring of multiple parameters, including voltage, current, temperature, and humidity. The constant flow of data contributes to the preservation of ideal operating circumstances. When combined with WSNs' powers to enable data-driven decision-making and real-time monitoring, these two technologies have the potential to revolutionize a wide range of applications. This paper offers a thorough examination of the significance of this synergy, including topics like environmental effect mitigation, grid resilience, and energy sustainability. In addition, the study analyzes the complex issues related to energy management, communication protocols, security, scalability, and integration with intermittent renewable sources that arise when synchronizing microgrids with WSNs. Future directions and new trends are also examined, including the growth of Internet of Things and AI-driven energy optimization. This study intends to assist academics, policymakers, and practitioners in realizing the full potential of microgrid implementations based on renewable energy for a more sustainable and interconnected future by illuminating the symbiotic link between microgrids and WSNs.
Due to the source and load prediction errors and uncertainties, the real operation state of microgrid may deviate significantly from the expected state, which leads to prevent the system from reaching its expected economic effects. In order to obtain the optimal economic effects for microgrid scheduling, an optimal microgrid scheduling model considered the demand responses is built in this paper firstly, and then a multi-time scale economic scheduling method based on day-ahead robust optimization and intraday model predictive control (MPC), is developed as well. Moreover, in the day-ahead stage, the long-time scale interval is set as 1 h and the robust optimization is used to address the low-frequency components in prediction errors and uncertainties. Meanwhile, the robust optimization enables to gain the day-head optimal economic scheduling plan for the microgrid and to keep the system operating effectively even when large-scale fluctuations happen. Furthermore, in the intraday stage, the short-time scale interval is set as 15 mins and MPC is adopted to track and correct the day-ahead economic scheduling plan, which enables to address the high-frequency components in prediction errors and uncertainties. Finally, simulation results demonstrate the feasibility of the proposed optimal microgrid scheduling model and the validity of the proposed multi-time scale economic scheduling method.
The implementation of real-time price-based demand response program and integration of renewable energy resources (RESs) improves efficiency and ensure stability of electric grid. This paper proposes a novel intelligent optimization based demand-side management (DSM) framework for smart grid integrated with RESs. In the intelligent DSM framework the artificial neural network (ANN) forecasts energy usage behavior of consumers and real-time price-based demand response program (RTPDRP) of electric utility company (EUC). The smart energy management controller of the proposed intelligent DSM framework adapts forecasted energy usage behavior of consumers using forecasted RTPDRP to create operation schedule. The consumers implement the created schedule to minimize energy cost, peak load, carbon emission subjected to improving user comfort and avoiding rebound peaks. Simulations are conducted using our proposed hybrid genetic ant colony (HGAC) optimization algorithm to create schedule for three cases: EUC without RESs, EUC with RESs, and EUC with both RESs and storage technologies. To endorse the applicability and productivity of the proposed DSM framework based on HGAC optimization algorithm with five existing algorithms based frameworks. Simulation results show that the proposed DSM framework is superior compared with the existing frameworks in terms of energy cost minimization, peak load mitigation, carbon emission alleviation, and user discomfort minimization. The proposed HGAC optimization algorithm reduced electricity cost, carbon emission, and peak load by 12.16%, 4.00%, and 19.44% in case I; by 26.8%, 20.71%, and 33.3% in case II; and by 24.4%, 16.44%, and 37.08% in case III, respectively, compared to without scheduling.
This paper proposes a two-layer in-depth secured management architecture for the optimal operation of energy internet in hybrid microgrids considering wind turbines, photovoltaics, fuel cell unit, and microturbines. In the physical layer of the proposed architecture, the operation of the grid is formulated as a single objective problem that is solved using teacher learning-based optimization (TLBO). Regarding the cyber layer of the proposed architecture, a two-level intrusion detection system (IDS) is proposed to detect various cyber-attacks (i.e. Sybil attacks, spoofing attacks, false data injection attacks) on wireless-based advanced metering infrastructures. The sequential probability ratio testing (SPRT) approach is utilized in both levels of the proposed IDS to detect cyber-attacks based on a sequence of anomalies rather than only one piece of evidence. The feasibility and performance of the proposed architecture are examined on IEEE 33-bus test system and the results are provided for both islanded and grid-connected operation modes.
This paper presents a new model for the self-scheduling problem using a home energy management system (HEMS), considering the presence of solar photovoltaic (PV) panels and an air conditioner (AC). The AC used in this study utilizes an inverter for controlling the temperature. The proposed model adopts the time-of-use (TOU) tariff for electricity, and minimization of the daily bill is the main objective of the proposed model. In the proposed model, there are some fixed and flexible loads, and optimal scheduling of the home appliances is modeled as a mixed-integer linear programming (MILP) problem to achieve the minimum daily bill. The HEMS in this paper incorporates a PV system, combined with the electrical energy storage (EES) system to deal with the uncertain solar power generation as well as optimally serving the loads during the peak hours, taking into account the charging and discharging control strategy of the installed EES. The indoor temperature can be held within a predefined margin and the settings of the AC system may be obtained according to the indoor-outdoor temperature model, which has been presented in this paper. During peak hours, the contribution of the AC in energy consumption to the daily bill is considerable. Therefore, optimal operation of the AC and other home appliances can effectively reduce the energy consumption and consequently, will result in reduced bills.
This article presents a fuzzy framework for smart home monitoring systems (FF-SHMS) effective in energy using the Internet of Things (IoT), demand monitoring, green energy conservation, energy conservation, and microgrids. The proposed fuzzy architecture is used to optimize the construction of intelligent microgrids with storage networks, sustainability, and resource-regulated loads. The fuzzy framework improves electricity and storage to use renewables and optimizes the economic reward of the microgrid. The fuzzy framework uses sun power, wind speed, and power load as input parameters to allow power efficiency controllably and uncontrolled. Input factors such as sunlight, electrical efficiency, wind, and energy are used to manage electricity using uncontrollable and regulated loads. These input data may be directly measured, imported, or estimated using any prediction method by grid calculation. This article contains the input parameters, a range of rules, and the de-fuzzified output by the expert method. The results of the expert program are mentioned to understand how to manage microgrid energy consumption and production. The judgements on generated electricity, load demand, and own use are dependent on three outputs. The first generation is for the treatment, sale, or consumption of the generated energy. The secondary output is used to check the loading. The third finding demonstrates how manufacturing can be performed to use by consumers. The expert procedure by entering parameter values on an hourly basis has been evaluated. The proposed solution is finally contrasted with other accessible alternatives to validate the results.
Installing thermal energy storage (TES) devices and utilizing the TES characteristic of heating networks are effective means of improving the flexibility of combined heat and power (CHP) systems. However, to truly take advantage of these, many factors such as the heat transfer (HT) processes, heat exchanger (HE) internal structure, HT area, mass flow rate, the HT delay are essential to be considered. These vital factors are not discussed in sufficient detail simultaneously and may lead to inaccurate modelling and underutilisation of thermal inertia. Thus, this paper investigates the HT process and thermal time-lag issues by analysing the structure and working characteristics of HE. A novel dispatch model considering HT delays is first established based on detailed HT processes. The developed model poses a strongly non-linear problem that is difficult to solve. Then, a decomposition-coordination method is proposed to deal with it. Furthermore, the sensitivity of different factors affecting HT delays is analysed so that the operator is able to configure parameters more clearly. Simulation results prove the necessity of considering the HT delay in CHP systems and the effectiveness of the proposed model. Further analyses also demonstrate that the proposed method offers higher accuracy than a solver and fitting method.
Effective simulation methods are becoming critically essential for the analysis of integrated energy systems (IESs) to reveal the interactions of multiple energy carriers. The incorporation of various energy technologies and numerous controllers make the IES a heterogeneous system, which poses new challenges to simulation methods. This paper focuses on the simulation of an IES with hybrid continuous-discrete properties and heterogeneous characteristics. First, a modified third-order quantized state system (MQSS3) method is proposed for the simulation of district heating systems (DHSs), in which quantized state system (QSS) and time-discretized integration are integrated to efficiently manage numerous discrete control actions. Second, an event-driven framework is established to integrate MQSS3 into the simulation of the electricity-heat integrated energy system (EH-IES). This framework enables the adoption of the most suitable models and algorithms for different systems to accommodate the heterogeneous properties of an IES. Case studies of an EH-IES with maximum 80% PV penetration and 210 buildings demonstrate that the dynamic interactions between the DHS and the power distribution network are accurately illustrated by the proposed simulation methods, in which MQSS3 indicates the highest simulation efficiency. It is also demonstrated in the simulation results that the flexibility from DHS can be utilized as demand-side resource to support the operation of power distribution network in aspects such as consuming the surplus PV generations.
With the deepening of energy market reform, market factors have more and more influence on the operation of energy system. The introduction of demand response mechanism guided by market factors in the optimization of regional integrated energy system (RIES) has become an inevitable trend. Based on the coupling characteristics of heterogeneous energy flow in regional integrated energy system and the response potential of flexible load on the user side, the concept of demand response (DR) in electricity market is extended to cooling and heat in energy system, and an optimal operation planning method of RIES considering demand response is proposed in this paper. In order to solve the problem that the existing evaluation system of energy system is not comprehensive, this paper constructs a regional integrated energy system model considering demand response, which takes the optimal comprehensive evaluation index consisting of economic benefits of both supply side and user side, load peak valley ratio and carbon emission as the objective function. Then, an improved genetic optimization algorithm is used to solve the model, obtaining the optimization results of load demand response and energy networks, which specifically includes the equipment installed capacity, hourly output as well as operation strategy. Finally, an industrial park in Guangzhou is selected for case study. The result shows that user-side demand response reduces the total cost of regional integrated energy system by 5.17% and the peak-to-valley ratio of electric load by 59.9%, which plays a role in cutting peak and filling valley in the energy system.
Integrated energy system (IES) is considered an effective way to alleviate energy supply pressure and improve energy efficiency, which has attracted considerable attentions worldwide. However, in existing studies, the coordination of uncertainty addressing and demand responses over different scheduling stages have not been fully considered in IES operation. Based on these considerations, a two-stage optimal operation method considering multiple uncertainties and integrated demand response is proposed for a community integrated energy system (CIES). First, given the CIES structure, various energy equipment are modeled and analyzed from the perspective of energy conversion and storage. Moreover, a generic optimal operation framework and model that comprises day-ahead and intraday stages is developed for a two-stage CIES scheduling. While the day-ahead stage introduces the time-shifted demand response (DR) to establish a robust optimization operation model, the intraday stage performs a stochastic scheduling with the adoption of replaced DR. Finally, a case study is carried out to demonstrate the operation model, and simulation results show that the proposed method can give play to the complementary advantages of multi energy sources and effectively promote the energy supply and demand balance in the presence of multiple uncertainties and demand responses.