Assessing the Constraints and Opportunities for Private Sector Participation in Activities Implemented Jointly: Two Case Studies From the U.S. Initiative for Joint Implementation

This paper assesses the constraints and opportunities for private-sector participation in Activities Implemented Jointly under the United Nations Framework Convention on Climate Change. After some initial background, the discussion turns to the United States Initiative on Joint Implementation (USIJI)—its objectives, proposal review and evaluation criteria, and a classification of project proposals by project type and stage of development. Two USIJI projects are developed as case studies. One case is an energy end use project that has gained formal acceptance and financing. The other case is an energy production project proposal that has not secured acceptance or financing. In both cases, transaction costs were substantial, and project proponents regarded gaining formal host country acceptance as the principal impediment to project development. The cases illustrate how the host country JI project approval process can become entangled in broader struggles over economic reforms. The cases also suggest that JI project proponents may have divergent perspectives on the speculative value of greenhouse gas (GHG) credits. An enforceable cap on GHG emissions in the project funders’ countries, which is a prerequisite to establishing any market for the credits, is contrary to the position of energy and power suppliers who promote voluntary emissions reductions. For emissions reduction technology firms, however, establishing a value for GHG credits would help generate demand for the firms’ stock in trade. Finally, the study underscores that notwithstanding transaction costs associated with JI proposal development and acceptance, financing remains the ultimate hurdle to project implementation.

Flexible operation of supercritical power plant via integration of thermal energy storage

© 2018 The Author(s).This chapter presents the recent research on various strategies for power plant flexible operations to meet the requirements of load balance. The aim of this study is to investigate whether it is feasible to integrate the thermal energy storage (TES) with the thermal power plant steam-water cycle. Optional thermal charge and discharge locations in the cycle have been proposed and compared. Dynamic modeling and simulations have been carried out to demonstrate the capability of TES integration in supporting the flexible operation of the power plant. The simulation software named SimuEngine is adopted, and a 600 MW supercritical coal-fired power plant model is implemented onto the software platform. Three TES charging strategies and two TES discharging strategies are proposed and verified via the simulation platform. The simulation results show that it is feasible to extract steam from steam turbines to charge the TES and to discharge the stored thermal energy back to the power generation processes. The improved capability of the plant flexible operation is further studied in supporting the responses to the grid load demand changes. The results demonstrated that the TES integration has led to much faster and more flexible responses to the load demand changes.Peer reviewe

Adaptive-predictive control strategy for HVAC systems in smart buildings – A review

Abstract High share of energy consumption in buildings and subsequent increase in greenhouse gas emissions along with stricter legislations have motivated researchers to look for sustainable solutions in order to reduce energy consumption by using alternative renewable energy resources and improving the efficiency in this sector. Today, the smart building and socially resilient city concepts have been introduced where building automation technologies are implemented to manage and control the energy generation/consumption/storage. Building automation and control systems can be roughly classified into traditional and advanced control strategies. Traditional strategies are not a viable choice for more sophisticated features required in smart buildings. The main focus of this paper is to review advanced control strategies and their impact on buildings and technical systems with respect to energy/cost saving. These strategies should be predictive/responsive/adaptive against weather, user, grid and thermal mass. In this context, special attention is paid to model predictive control and adaptive control strategies. Although model predictive control is the most common type used in buildings, it is not well suited for systems consisting of uncertainties and unpredictable data. Thus, adaptive predictive control strategies are being developed to address these shortcomings. Despite great progress in this field, the quantified results of these strategies reported in literature showed a high level of inconsistency. This is due to the application of different control modes, various boundary conditions, hypotheses, fields of application, and type of energy consumption in different studies. Thus, this review assesses the implementations and configurations of advanced control solutions and highlights research gaps in this field that need further investigations

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Implementing the Clean Development Mechanism: Lessons from U.S. Private-Sector Participation in Activities Implemented Jointly

The "Clean Development Mechanism" (CDM) contained in the December 1997 Kyoto Protocol to the United Nations Framework Convention on Climate Change provides, for the first time, the capacity for industrialized countries to claim credits for greenhouse gas (GHG) emissions reductions or offsets undertaken in cooperation with host developing countries. However, the Protocol provides no guidance on how these cooperative activities for GHG reduction and sustainable development would be undertaken in practice, including the particularly important issue of the relationship of the private sector vis-à-vis government institutions in designing, financing, and securing approval for jointly implemented GHG abatement projects. The pilot program for "Activities Implemented Jointly" under the Framework Convention provides an opportunity to better understand the practical constraints and opportunities for successful private sector participation in the CDM. This paper highlights some of the lessons for establishing a successful CDM by examining a small number of cases from the United States Initiative on Joint Implementation (USIJI). The authors first review the objectives, proposal review and evaluation criteria of this program, and provide some overall information on project proposals by project type and stage of development. They then develop case studies of two energy-related USIJI projects from the earlier phase of the program. These cases illustrate several potential problems that can arise in establishing CDM transactions. Further investigation of more recent cases sheds some light on the extent to which these problems change over time. To be successful, the CDM must be based on a solid institutional footing, with clear incentives for all parties involved. The cases examined here illustrate how transactions can become entangled in the same kinds of problems that bedevil other transactions in developing and transitional economies. In both early cases, "transaction costs" were substantial. The latter projects indicated that while the nature of transactions costs changed over time, they still remained somewhat substantial. Project proponents regarded gaining USIJI acceptance as one of the principal impediments to JI project development. The cases also illustrate the need for clear and widely understood goals and procedures for investor country approval. In addition, the analysis underscores how attitudes of different project proponents regarding the value of GHG credits can affect their perspective on the transaction. Finally, the study underscores that financing remains the ultimate hurdle to project implementation, and that the expectation of a clear financial return on investment is a prerequisite to a successful project.

Performance Investigation and Adaptive Neuro-Fuzzy Prediction of Building Integrated Straight-Bladed Vertical Axis Wind Turbine

This paper presents the performance investigation and adaptive neuro-fuzzy prediction of a building integrated straight-bladed vertical axis wind turbine (VAWT). An experiment was conducted with the VAWT integrated on the building rooftop. The coefficient of power of the VAWT was predicted using adaptive neuro-fuzzy inference system (ANFIS). The input variables for the model development include the rotational speed, angular velocity, and tip speed ratio, while coefficient of power is the output. In the fuzzy logic of the fuzzy inference system (FIS), the parameter of the membership function is adjusted by the neural network in ANFIS. MATLAB/Simulink was used to implement this intelligent algorithm and the performance was investigated using root mean square error (RMSE) and coefficient of determinant (R2). In addition, the ANFIS technique precision was evaluated against the results of the experiment. The result obtained indicates that the maximum coefficient of power (Cpmax) was obtained at about Y = 250 mm above the building rooftop. Furthermore, it was also established that the developed ANFIS model is very effective and reliable in predicting the performance of building integrated straight-bladed VAWT

Cooling load estimation using machine learning techniques

Estimating cooling loads in heating, ventilation, and air-conditioning (HVAC) systems is a complex task. This is mainly due to its dependence on numerous factors which are both intrinsic and extrinsic to buildings. These include climate, forecasts, building material, fenestration etc. In addition, these factors are non-linear and time-varying. Therefore, capturing the effect of these parameters on the cooling load is a complex task. This investigation combines forward modelling, i.e., physics based model simulated using energyPlus with deep-learning techniques to build a cooling load estimator. The forward model captures all the time-varying factors influencing the cooling loads. We use the long short-term memory (LSTM), a deep-learning method to provide forecasts of cooling loads. The advantage of the proposed approach is that cooling load estimations can be provided in real-time thus providing sort of soft-sensor for estimating cooling loads in buildings. The proposed approach is illustrated on a building of suitable scale and our results demonstrates the ability of the tool to provide forecasts

APPLICATION OF SOFT COMPUTING TECHNIQUES OVER HARD COMPUTING TECHNIQUES: A SURVEY

Soft computing is the fusion of different constituent elements. The main aim of this fusion to solve real-world problems, which are not solve by traditional approach that is hard computing. Actually, in our daily life maximum problem having uncertainty and vagueness information. So hard computing fail to solve this problems, because it give exact solution. To overcome this situation soft computing techniques plays a vital role, because it has capability to deal with uncertainty and vagueness and produce approximate result. This paper focuses on application of soft computing techniques over hard computing techniques

Additional controls to enhance the active power management within islanded microgrids

Balancing the generated and consumed power in a microgrid is highly affected by the varying output power of the intermittent, renewable energy-based, distributed energy resources. This paper focuses on coordinating the output power among the energy resources within a microgrid while managing the consumed power at the demand side. The considered microgrid in this study consists of a battery system, which is the primary unit for grid-forming, as well as a photovoltaic system as the grid-following unit. A soft starting ramp function and an active power reduction function are implemented within the photovoltaic inverter respectively for the periods after the isolation of the microgrid from the grid and the over-frequency observation. Meanwhile, a demand-side management is developed based on the level of the battery’s state of charge, to facilitate the microgrid with a longer time in supplying its critical loads