Energy Purchase Research Articles

Day-Ahead Optimal Interval Scheduling for Building Energy System Considering Building Envelope Virtual Energy Storage Uncertainties

The heat storage property of building envelopes is usually modeled into virtual energy storage (VES) and regarded as a flexibility resource to support the energy scheduling of building energy systems (BESs). However, the adjustable potential of VES is uncertain, incurred by several ambient random variables with/without specific probability distributions, posing challenges in determining the operational planning schemes of the BES. This article is intended to study a day-ahead optimal scheduling method for a PV-integrated BES (known as PV-BES) with the consideration of VES using interval optimization methods. First, an interval number is used to characterize the uncertainties of outdoor temperature, light irradiance, and the occupant’s behavior reflected by the uncontrollable household load. Second, an interval VES model is developed by modeling VES’s virtual charge–discharge power (VCDP) with an interval number. Finally, a day-ahead optimal interval scheduling model for the PV-BES considering VES is formulated, aiming to minimize the electricity energy purchase cost of the PV-BES from the external grid. Numerical simulation is conduced, and the results validated the effectiveness of the proposed method.

A comparative techno-economic and sensitivity analysis of Power-to-X processes from different energy sources

In this paper, green hydrogen produced via water electrolysis and its conversion into three alternative fuels, such as methane, methanol, and ammonia, are considered. The four different Power-to-Fuel solutions are investigated and compared from both the technical and economic points of view aiming at providing a comprehensive overview of the Power-to-Fuel feasibility. At first, the global process efficiency, the storage capacity, the annual costs, and the production cost of the different fuels (in terms of mass, energy, and hydrogen content) are calculated for a reference scenario. Then, a sensitivity analysis is carried out analyzing the influence of many parameters (i.e. electricity cost, electrolyser CAPEX, operating hours, etc) on the economic viability of all the processes. Finally, map plots are developed reporting the fuel production cost for considering different renewable energy sources and their availability. They can be considered as a useful tool for pre-feasibility analysis of power to fuel processes enabling to analyze and compare the different solutions in different scenarios. It is found that the highest efficient process is the Power-to-Hydrogen (about 61.5%) followed by the methanol and ammonia processes and in the end the methane processes. In terms of energy storage and energy density by volume, the methanol resulted in the most suitable solution, while the ammonia resulted in the best H2 storage medium in terms of kg of H2 per m3 of storage (108 kgH2/m3). From the economic perspective, the annual cost breakdown showed that, in all the cases, the major expenditures are related to the electrical energy purchase and CAPEX and OPEX of the electrolyzer (around 90% of total costs), and a 50% reduction in electricity cost and electrolyzer CAPEX could lead to a reduction of about 30% and 18% on fuel production cost, respectively. The cheapest fuel in terms of mass and energy content are methanol (1.02 €/kg) and Hydrogen (0.16€/kWh), respectively. The ammonia production cost in terms of hydrogen content in mass resulted almost comparable with the Hydrogen one (5.76€/kgH2 and 5.31€/€/kgH2, respectively). The contribution of the co-produced oxygen sale has been estimated in around a 15% reduction of fuel production cost in all the cases.

Economic Evaluation of Implementation of Power-to-Gas: Application to the Case of Spain

An economic analysis of the implementation of the power-to-gas (PtG) system between 2030 and 2055 is presented at a large scale. The capacity of the PtG system is adapted to two scenarios in Spain (Bailera and Lisbona 2018), corresponding to growing scenarios of 1.73 and 1.36%/y of its electricity market. The total power capacity of the PtG system has been fixed to 12.7 and 3.84 GW, respectively, at the end of 2055. The levelized cost of storage (LCOS) of the implementation of PtG has been evaluated. Assuming uncertainties in the current cost projections for CAPEX and OPEX, LCOS estimations are between 136 and 686 EUR/MWh with a payback time of 16 years in the best scenario for a reference electricity purchase of 100 EUR/MWh and a CO2 penalty of 100 EUR/ton. A sensitivity analysis and the viability dependence versus energy purchase and CO2 penalty certificates is shown. This work sheds some light for the comparison of PtG implementation costs in comparison with other storage options, such as batteries, pumped-storage hydroelectricity or compressed air storage for future energy scenarios.

Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system (ANFIS)

The aim of this study is to develop very short-term and accurate energy consumption forecasts for educational building. The purpose is to develop a predictive model for building energy planning to balance the supply from renewable power systems and the building electrical load demand). For the methodology, an adaptive neuro-fuzzy inference system (ANFIS) was used as a machine learning approach for building energy forecasting. The data for training (80%), testing (10%), and validation (10%) was used from smart energy meters installed in the building and weather data. A total of 20520 dataset instances was used for developing the forecasting model. The originality of this study is the ANFIS model was validated for the training, testing, and validation data, and the accuracy and reliability of the forecasting model was assessed over various very short time horizon of past data (0.5–4 h ahead). The main results reveal that the predictive model is extremely accurate in predicting the energy consumption of a building. The values of coefficient of correlation R for training, testing, and validation are 0.98017, 0.9778, and 0.97593, respectively, for the 30 min ahead energy forecasting. The R values for all the data are respectively 0.97951, 09854, and 0,96778 for the 0.5, 1, 4 h ahead energy forecasting. The low nMSE and nMAE errors for the ANFIS model demonstrate how well the model replicates the reported results from smart energy meters. The research findings are very important for the energy planning of microgrid power systems (energy purchase, building operations and maintenance, and demand side management).

Optimal Placement and Sizing of PV Sources in Distribution Grids Using a Modified Gradient-Based Metaheuristic Optimizer

The problem of the optimal placement and sizing of renewable generation sources based on photovoltaic (PV) technology in electrical distribution grids operated in medium-voltage levels was studied in this research. This optimization problem is from the mixed-integer nonlinear programming (MINLP) model family. Solving this model was achieved by implementing a master–slave optimization approach, where the master–slave corresponded to the application of the modified gradient-based metaheuristic optimizer (MGbMO) and the slave stage corresponded to the application of the successive approximation power flow method. In the master stage, the problem of the optimal placement and sizing of the PV sources was solved using a discrete–continuous codification, while the slave stage was used to calculate the objective function value regarding the energy purchasing costs in terminals of the substation, as well as to verify that the voltage profiles and the power generations were within their allowed bounds. The numerical results of the proposed MGbMO in the IEEE 34-bus system demonstrated its efficiency when compared with different metaheuristic optimizers such as the Chu and Beasley genetic algorithm, the Newton metaheuristic algorithm, the original gradient-based metaheuristic optimizer, and the exact solution of the MINLP model using the general algebraic modeling system. In addition, the possibility of including meshed distribution topologies was tested with excellent numerical results.

Optimal dispatch of integrated energy system considering integrated demand response resource trading

In order to reduce the impact of renewable energy output and load fluctuations and improve the flexibility of the integrated energy system (IES), it is necessary to further promote user participation in integrated demand response (IDR). Therefore, this paper constructs a hierarchical framework that enables the transaction of IDR resources among users by combining the information interaction network established by blockchain and the energy management network established by energy management system. This paper also analyses the comfort of users, the cost of energy purchase by users and the cost of energy use by load aggregators, and then develops a two-layer optimization model. The results of the simulation show that the model and trading framework constructed in this paper can realize the trading of IDR resources among users, which effectively promotes the participation of users in IDR, reduces the cost of users and load aggregators, reduces the loss of IDR resources, enables more effective integration of dispersed IDR resources and improves the flexibility of IES.

Improved Particle Swarm Algorithm Using Rubik’s Cube Topology for Bilevel Building Energy Transaction

Abstract Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush–Kuhn–Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik’s Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik’s Cube topology-based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology-based PSO and its scalability on larger scale applications.

Changes in household food and drink purchases following restrictions on the advertisement of high fat, salt, and sugar products across the Transport for London network: A controlled interrupted time series analysis.

Restricting the advertisement of products with high fat, salt, and sugar (HFSS) content has been recommended as a policy tool to improve diet and tackle obesity, but the impact on HFSS purchasing is unknown. This study aimed to evaluate the impact of HFSS advertising restrictions, implemented across the London (UK) transport network in February 2019, on HFSS purchases. Over 5 million take-home food and drink purchases were recorded by 1,970 households (London [intervention], n = 977; North of England [control], n = 993) randomly selected from the Kantar Fast Moving Consumer Goods panel. The intervention and control samples were similar in household characteristics but had small differences in main food shopper sex, socioeconomic position, and body mass index. Using a controlled interrupted time series design, we estimated average weekly household purchases of energy and nutrients from HFSS products in the post-intervention period (44 weeks) compared to a counterfactual constructed from the control and pre-intervention (36 weeks) series. Energy purchased from HFSS products was 6.7% (1,001.0 kcal, 95% CI 456.0 to 1,546.0) lower among intervention households compared to the counterfactual. Relative reductions in purchases of fat (57.9 g, 95% CI 22.1 to 93.7), saturated fat (26.4 g, 95% CI 12.4 to 40.4), and sugar (80.7 g, 95% CI 41.4 to 120.1) from HFSS products were also observed. Energy from chocolate and confectionery purchases was 19.4% (317.9 kcal, 95% CI 200.0 to 435.8) lower among intervention households than for the counterfactual, with corresponding relative reductions in fat (13.1 g, 95% CI 7.5 to 18.8), saturated fat (8.7 g, 95% CI 5.7 to 11.7), sugar (41.4 g, 95% CI 27.4 to 55.4), and salt (0.2 g, 95% CI 0.1 to 0.2) purchased from chocolate and confectionery. Relative reductions are in the context of secular increases in HFSS purchases in both the intervention and control areas, so the policy was associated with attenuated growth of HFSS purchases rather than absolute reduction in HFSS purchases. Study limitations include the lack of out-of-home purchases in our analyses and not being able to assess the sustainability of observed changes beyond 44 weeks. This study finds an association between the implementation of restrictions on outdoor HFSS advertising and relative reductions in energy, sugar, and fat purchased from HFSS products. These findings provide support for policies that restrict HFSS advertising as a tool to reduce purchases of HFSS products.

Low-Carbon Economic Dispatch for Integrated Energy System Through the Dynamic Reward and Penalty Carbon Emission Pricing Mechanism

The integrated energy system (IES) plays a key role in energy conservation and emission reduction. In order to realize the low-carbon economic dispatch of the IES, current studies usually utilize the fixed and the ladder-type pricing mechanism-based carbon emission unit cost (CEUC) in the optimal dispatch model. However, those mechanisms fail to take carbon emissions levels of the system into account, and the relevant parameters, such as the interval length, are hard to set. In order to tackle this challenge, a CEUC model with a dynamic reward and penalty pricing mechanism (DRPPM) is constructed. And then an optimal dispatch model to minimize the comprehensive operation cost, including the carbon emission cost, the energy purchasing cost, and the equipment operation cost, is proposed. At last, an actual electricity–heat–cooling–gas IES is employed to analyze the impact of parameters of the CEUC model on the dispatch.

Optimal Energy Scheduling Based on Jaya Algorithm for Integration of Vehicle-to-Home and Energy Storage System with Photovoltaic Generation in Smart Home.

With the emerging of the smart grid, it has become easier for consumers to control their consumption. The efficient use of the integration of renewable energy sources with electric vehicle (EV) and energy storage systems (ESSs) in the smart home is a popular choice to reduce electricity costs and improve the stability of the grid. Therefore, this study presents optimal energy management based on the Jaya algorithm for controlling energy flow in the smart home that contains photovoltaic generation (PV), integrated with ESS and EV. The objective of the proposed energy management is to reduce electricity cost while meeting the household load demand and energy requirement for the EV trip distance. By using the Jaya algorithm, the modes of home-to-vehicle (H2V) and vehicle-to-home (V2H) are controlled, in addition to controlling the purchase of energy from the grid and sale of the energy to the grid from surplus PV generation and ESS. Before EV participation in the V2H process, the amount of energy stored in the electric vehicle battery will be verified to be more than the energy amount required for the remaining EV trip to ensure that the required energy for the remaining EV trip is satisfied. Simulation results highlight the performance of the optimal energy scheduling to achieve the reduction of the daily electricity cost and meeting of load demand and EV energy required. The simulation results prove that optimal energy management solutions can be found with significant electricity cost savings. In addition, Jaya is compared with the particle swarm optimization (PSO) algorithm in order to evaluate its performance. Jaya outperforms PSO in terms of achieving optimal energy management objectives.

Are the current incentives sufficient to drive the use of solar PV in the Colombian residential sector? - An analysis from the perspective of the game theory

It is necessary to increase the use of renewable energy sources to supply the world's energy demand to mitigate the harmful effects of climate change. Many countries have incorporated the use of solar energy on a large scale. However, the use of photovoltaic (PV) systems at the residential level has been rather limited because many barriers to their implementation persist (e.g., high investment costs, insufficient government policies and incentives, and lack of knowledge in the implementation and operation of these systems). This paper aims to investigate how the interaction between prosumers and consumers can drive the use of PV systems at the residential level. The interaction is analyzed from the field of game theory. The payoffs of the players' strategies determine not only the Nash equilibrium but also the desirability of the interaction for both investment in the case of prosumer and consumption costs in the case of the consumer. Prosumer strategies consisted of selling power to the grid and to the consumer; selling only to the consumer; selling only to the grid; selling to the grid and giving to the consumer; and only giving to the consumer. The consumer strategies were to buy only from the grid or to buy from both the grid and the prosumer. The prosumer's investment in three sizes of PV systems (2.3, 2.7 and 3.6 kW) were analyzed; the amount of electricity produced was estimated using PVWatts and data from a Typical Meteorological Year in the city of Barranquilla, Colombia. The Net Present Value of each investment was calculated considering the prosumer's strategies and this indicator was taken to an equivalent annual cost to represent the prosumer's payment in the Nash equilibrium. On the other hand, for the consumer's payments, the annual energy purchase costs to supply their demand were used, according to the consumer's strategies. Analysis found that PV systems that can produce surpluses can be attractive to the prosumer if the tax on revenues received from the sale of energy to the grid is eliminated. Profitability is further improved if the prosumer is allowed to sell their surplus to other consumers, with the purchase price to the grid being the maximum price at which they can sell to other consumers. In turn, the cost savings for consumers will be greater to the extent that they can buy cheaper energy from prosumers. Based on these findings, countries such as Colombia, where there has not been a massive use of solar photovoltaic energy at the residential level, can stimulate investments in this sector by eliminating the income tax on the sales of surplus energy of small prosumers, without representing a loss of income for these countries.

Acceleration of Clean Energy Use Based on The 2015 Paris Agreement

The adverse effects of climate change are a major threat to human life and the environment. Indonesia's participation in the 2015 Paris Agreement encourages changes in people's lifestyles to become environmentally friendly. Indonesia commits to reducing emissions before 2030 . The purpose of this study is to describe energy regulation within the scope of the provisions of national law and international law and to examine Indonesia's efforts to accelerate the use of clean energy based on the 2015 Paris Agreement. The approach used is normative. The data collection technique is through conventional literature searches and online and analyzed with qualitative data analysis because the data will be presented in a narrative-descriptive manner, not in numerical or numerical form. The results of the research show that the regulation of energy within the scope of national legal provisions is generally regulated in the Law of the Republic of Indonesia Number 30 of 2007 concerning Energy. This law contains general problems of energy resources in Indonesia from philosophical, juridical, and sociological aspects. This law has the function of a legal umbrella for other laws and regulations that will regulate energy-related matters. Indonesia's efforts to accelerate the use of clean energy based on the 2015 Paris Agreement, namely by ratifying the 2015 Paris Agreement into Law of the Republic of Indonesia Number 16 of 2016. To accelerate the use of new and renewable energy as clean energy, the Indonesian government issued a Draft Presidential Regulation that regulates the purchase of new and renewable energy. clean energy electricity and several other efforts.

Acceleration of Clean Energy Use Based on The 2015 Paris Agreement

The adverse effects of climate change are a major threat to human life and the environment. Indonesia's participation in the 2015 Paris Agreement encourages changes in people's lifestyles to become environmentally friendly. Indonesia commits to reducing emissions before 2030 . The purpose of this study is to describe energy regulation within the scope of the provisions of national law and international law and to examine Indonesia's efforts to accelerate the use of clean energy based on the 2015 Paris Agreement. The approach used is normative. The data collection technique is through conventional literature searches and online and analyzed with qualitative data analysis because the data will be presented in a narrative-descriptive manner, not in numerical or numerical form. The results of the research show that the regulation of energy within the scope of national legal provisions is generally regulated in the Law of the Republic of Indonesia Number 30 of 2007 concerning Energy. This law contains general problems of energy resources in Indonesia from philosophical, juridical, and sociological aspects. This law has the function of a legal umbrella for other laws and regulations that will regulate energy-related matters. Indonesia's efforts to accelerate the use of clean energy based on the 2015 Paris Agreement, namely by ratifying the 2015 Paris Agreement into Law of the Republic of Indonesia Number 16 of 2016. To accelerate the use of new and renewable energy as clean energy, the Indonesian government issued a Draft Presidential Regulation that regulates the purchase of new and renewable energy. clean energy electricity and several other efforts.

Acceleration of Clean Energy Use Based on The 2015 Paris Agreement

The adverse effects of climate change are a major threat to human life and the environment. Indonesia's participation in the 2015 Paris Agreement encourages changes in people's lifestyles to become environmentally friendly. Indonesia commits to reducing emissions before 2030 . The purpose of this study is to describe energy regulation within the scope of the provisions of national law and international law and to examine Indonesia's efforts to accelerate the use of clean energy based on the 2015 Paris Agreement. The approach used is normative. The data collection technique is through conventional literature searches and online and analyzed with qualitative data analysis because the data will be presented in a narrative-descriptive manner, not in numerical or numerical form. The results of the research show that the regulation of energy within the scope of national legal provisions is generally regulated in the Law of the Republic of Indonesia Number 30 of 2007 concerning Energy. This law contains general problems of energy resources in Indonesia from philosophical, juridical, and sociological aspects. This law has the function of a legal umbrella for other laws and regulations that will regulate energy-related matters. Indonesia's efforts to accelerate the use of clean energy based on the 2015 Paris Agreement, namely by ratifying the 2015 Paris Agreement into Law of the Republic of Indonesia Number 16 of 2016. To accelerate the use of new and renewable energy as clean energy, the Indonesian government issued a Draft Presidential Regulation that regulates the purchase of new and renewable energy. clean energy electricity and several other efforts.

Single Axis Sun Tracking Solar System Using IC L293D

Of all the renewable energies, solar power is the only energy gained its popularity and importance quickly. Through the solar tracking system, we are able to manufacture a plenteous quantity of energy that makes the solar panel’s workability far more efficient. Perpendicular quotient of the solar panels with the sun rays are that the reason lying behind its efficiency. Pecuniary, its installation charges are high provided cheaper choices are on the market. This project is mentioned all regarding the design and construction mechanism of the paradigm for the sun solar tracking system having a single axis of freedom. The main control circuit is based on upon motor controlling ICl293d. The LDR sensors, in accordance with the detection of the sun rays, will provide direction to the DC Motor that during which manner the solar panel goes to revolve. Through this, the solar panel is positioned in such a way that the utmost quantity of sun rays could be received as compared with the other motors, DC motor is simplest and therefore the suave one, the torque of that is high and speed of the motor is slow enough. We can program it for ever-changing the direction however the very fact that it rotates solely in one direction subject to exception as far as programming is concerned. 1985, initial time ever it had been witnessed for production of the silicon solar cells with an efficiency of 20 % percentage, although a hike within the efficiency of the solar panel had a handsome increase still perfection was a far fetching goal for it. Below 40%, most of the panels still hover to control. Consequently, peoples are compelled to get a variety of panels to meet their energy demands or purchase single systems with giant outputs. Availability of the solar cells varieties with higher efficiencies is on provided they're too expensive to buy. Ways to be accessed for increasing solar panel efficiencies are a plethora in range still one among the ways to be availed for accomplishing the said purpose while reducing prices, is chasing. Tracking helps in the wider projection of the panel to the Sun with enlarged power output. It may well be twin or single.

Low-carbon optimal planning of an integrated energy station considering combined power-to-gas and gas-fired units equipped with carbon capture systems

To deal with the menace of global warming and energy crisis, low-carbon and sustainable energy consumption patterns are receiving more and more attention. Carbon capture and storage technologies, renewable energy sources and multi-energy systems are important measures to achieve low-carbon emission and sustainable energy development. In multi-energy systems, an integrated energy station is a key to realize the coupled and coordinated operation of multi-energy networks. This paper proposes an integrated energy station optimal planning model which considers the combined power-to-gas and gas-fired units equipped with carbon capture systems. To describe the closed loops of energy flows in the proposed integrated energy station, this paper presents an improved energy hub formulation based on the directed acyclic graph. Moreover, the output ports of the integrated energy station is connected to multi-energy networks to sell excess energy. The improved energy hub formulation is applied in the above-mentioned optimal planning model. The objective of the proposed optimal planning model is to minimize the total cost of integrated energy station, which consists of the investment cost, energy purchase cost, energy sales revenue and carbon cost. Finally, case studies are carried out to discuss and validate the effectiveness of the proposed optimal planning model and the improved energy hub formulation.

Multi-Objective Approach for Managing Uncertain Delivery from Renewable Energy Sources within a Peer-to-Peer Energy Balancing Architecture

On the energy markets, conscious customers may exist who are not only interested in minimising the cost of energy purchase, but, simultaneously, in optimising some other quality criteria (arising from ecological concerns, or social responsibility of the energy producers). In this paper, we develop both a mathematical optimisation problem and a market framework for balancing a power system in a peer-to-peer market setup, where product differentiation can be considered directly on the market. Thus, origins of energy may be clearly identified, and product quality characteristics can be understood by various actors (including households). We derive a multi-objective (mixed-integer) linear programming optimisation problem for balancing the energy system in a peer-to-peer energy trading environment, where not only the cost but also other additional quality criteria are considered. We have identified many possible actors to be present within the proposed market setup. They include consumers, producers, brokers and flexible prosumers with storage. The approach was tested on the IEEE 30-bus standard test system, over three different scenarios, by analysing the impact of various actors/peers activities and different extensions. It has been shown that a multi-objective energy balancing scheme may be developed through crafted optimisation problem and that each type of studied peers may bring some added value to the power system balancing.

Meeting the Net Zero Emissions Challenge – Alphabet’s Carbon Management Actions and Opportunities

The first step in managing an entity’s carbon emissions involves measurement as ‘what gets measured gets managed’ and measurement lies at the heart of carbon management. The study of carbon management is important to all stakeholders as it enables calculation of emissions that can help determine opportunities for emissions reductions to address climate change which can improve energy efficiency and brand reputation. Alphabet is an interesting company to study given its main commercial activities relate to the development and use of information technology and provision of online services. It relies heavily on online advertising for its revenue but also develops new technologies to make its operations more efficient which also have commercial applications. Also, although not directly involved in manufacturing hardware, its hardware suppliers and products generate significant emissions through their manufacturing and lifecycles. Alphabet, formerly Google, claims to have net zero emissions through carbon management by improving efficiency, purchases of renewable energy and offsets since 2007. As of 2019, their operational emissions were 5.7M t CO2e which they managed to reduce for the 3rd year in a row to zero. Alphabet will solely use carbon-free energy to power their operations by 2030 through investment in renewable energy. Furthermore, they plan to invest in renewable energy projects in regions where their suppliers operate. Carbon management and addressing climate change is a challenge to Alphabet but it also provides it with opportunities through commercialisation and expansion of their technologies. This paper contributes to the field by reviewing how Alphabet estimate and reduce their reportable emissions and, in the process, create commercial opportunities for their technologies. The paper also highlights how a limited range of emissions are reported by Alphabet.

An efficient and economical storage and energy sharing model for multiple multi-energy microgrids

Multi-energy microgrids are facing a dilemma that realizing high local energy efficiency requires large-capacity ESS with hefty investment costs. To address the dilemma, an efficient and economic hybrid storage and energy sharing model for multiple microgrids is proposed. Specificly, a hybrid energy storage system (HESS) is introduced, which contains an electrical battery and a heat storage tank and is able to realize energy conversion. The multiple microgrids can share energy through the HESS in a collaborative way. An energy optimization problem is formulated to minimize the overall energy costs including energy purchase cost and HESS operating cost. ADMM algorithm is used to solve the problem in a distributed manner to avoid privacy concerns. The storage and energy sharing benefits of the microgrids and the HESS are determined by Nash bargaining solution. Simulation results show that the model can effectively improve the utilization of the renewable energy, and lead to considerable economic benefits for both the microgrids and the HESS.

Modular data model for energy-flexible cyber-physical production systems

Due to the massive expansion of renewable energies and the growing importance of carbon pricing, energy prices are rising, and industrial companies must put higher effort in purchasing electrical energy cost-efficiently. Additionally, the increasing share of renewable energies leads to more volatility in the power market, giving companies the opportunity to reduce their energy costs by using energy-flexibility measures. As thermal supply systems offer a great potential for energy flexibility, building automation systems which control the supply systems must be adapted for communication and external access. Intelligent algorithms interacting with the automation can enable factories to fexibilize their energy purchases and to participate in energy markets by using external information, such as energy prices or forecasts of the machine’s energy demand. This requires seamless communication that connects all hierarchy levels of the Reference Architectural Model Industry 4.0 (RAMI 4.0) from connected world to field level. Also functional safety must be ensured. Moreover, energy-flexibility measures must be validated within simulation environments which should show the same data structure. Therefore, a standardized data model that can be applied across different companies is necessary. We propose a concept for a modular and consistent data model for automation, simulation, and communication to enable simple data exchange using external algorithms. Therefore, base classes containing variables for generic actors and sensors are developed and extended by further variables to model systems based on their control mode. Moreover, a safety module to manage external access by algorithms is included. With this data model, industrial energy supply systems can be easily expanded to include additional components. Furthermore, energy supply systems consisting of several components, such as energy converters, pumps and valves can be modeled in an simple and time-efficient way. The data model is applied to a compression chiller at ETA Research Factory.