An innovation-focused roadmap for a sustainable global photovoltaic industry

The solar photovoltaic (PV) industry has undergone a dramatic evolution over the past decade, growing at an average rate of 48 percent per year to a global market size of 31. GW in 2012, and with the price of crystalline-silicon PV module as low as $0.72/W in September 2013. To examine this evolution we built a comprehensive dataset from 2000 to 2012 for the PV industries in the United States, China, Japan, and Germany, which we used to develop a model to explain the dynamics among innovation, manufacturing, and market. A two-factor learning curve model is constructed to make explicit the effect of innovation from economies of scale. The past explosive growth has resulted in an oversupply problem, which is undermining the effectiveness of "demand-pull" policies that could otherwise spur innovation. To strengthen the industry we find that a policy shift is needed to balance the excitement and focus on market forces with a larger commitment to research and development funding. We use this work to form a set of recommendations and a roadmap that will enable a next wave of innovation and thus sustainable growth of the PV industry into a mainstay of the global energy economy. © 2013 Elsevier Ltd

Middleware architectures for the smart grid: A survey on the state-of-the-art, taxonomy and main open issues

The integration of small-scale renewable energy sources in the smart grid depends on several challenges that must be overcome. One of them is the presence of devices with very different characteristics present in the grid or how they can interact among them in terms of interoperability and data sharing. While this issue is usually solved by implementing a middleware layer among the available pieces of equipment in order to hide any hardware heterogeneity and offer the application layer a collection of homogenous resources to access lower levels, the variety and differences among them make the definition of what is needed in each particular case challenging. This paper offers a description of the most prominent middleware architectures for the smart grid and assesses the functionalities they have, considering the performance and features expected from them in the context of this application domain

ASEAN grid flexibility: Preparedness for grid integration of renewable energy

In 2015, ASEAN established a goal of increasing its renewable energy share in its energy portfolio from approximately 13–23% by 2025. Renewable electricity, especially intermittent and variable sources, presents challenges for grid operators due to the uncertain timing and quantity of electricity supply. Grid flexibility, the electric grid's ability to respond to changing demands and supply, now stands a key resource in responding to these uncertainties while maximizing the cost-effective role of clean energy. We develop and apply a grid flexibility assessment tool to assess ASEAN's current grid flexibility using six quantitative indicators: grid reliability, electricity market access; load profile ramp capacity; quality of forecasting tools; proportion of electricity generation from natural gas; and renewable energy diversity. We find that ASEAN nations cluster into three groups: better; moderately; and the least prepared nations. We develop an analytical ramp rate calculator to quantify expected load ramps for ASEAN in an integrated ASEAN Power Grid scenario. The lack of forecasting systems and limited electricity market access represent key weaknesses and areas where dramatic improvements can become cost-effective means to increase regional grid flexibility. As ASEAN pursues renewable energy targets, regional cooperation remains essential to address identified challenges. Member nations need to increase grid flexibility capacity to adequately prepare for higher penetrations of renewable electricity and lower overall system costs

The role of large-scale energy storage design and dispatch in the power grid: A study of very high grid penetration of variable renewable resources

We present a result of hourly simulation performed using hourly load data and the corresponding simulated output of wind and solar technologies distributed throughout the state of California. We examined how we could achieve very high-energy penetration from intermittent renewable system into the electricity grid. This study shows that the maximum threshold for the storage need is significantly less than the daily average demand. In the present study, we found that the approximate network energy storage is of the order of 186. GW. h/22. GW (approximately 22% of the average daily demands of California). Allowing energy dumping was shown to increase storage use, and by that way, increases grid penetration and reduces the required backup conventional capacity requirements. Using the 186. GW. h/22. GW storage and at 20% total energy loss, grid penetration was increased to approximately 85% of the annual demand of the year while also reducing the conventional backup capacity requirement to 35. GW. This capacity was sufficient to supply the year round hourly demand, including 59 GW peak demand, plus a distribution loss of about 5.3%. We conclude that designing an efficient and least cost grid may require the capability to capture diverse physical and operational policy scenarios of the future grid. © 2014 Elsevier Ltd

Geospatial multi-criteria analysis for identifying high priority clean energy investment opportunities: A case study on land-use conflict in Bangladesh

Bangladesh is a globally important emerging economy with rapidly increasing energy demand. The Bangladeshi government's primary capacity expansion plan is to install 13.3 GW of new coal by 2021, including the 1.3 GW Rampal coal power plant to be developed in the Sundarbans. Inadequate geospatial and economic information on clean energy investment opportunities are often a significant barrier for policy makers. Our study helps fill this gap by applying a new method to assess energy investment opportunities, with focus on understanding land-use conflicts, particularly important in this context as Bangladesh is constrained on land for agriculture, human settlements, and ecological preservation. By extending a geospatial multi-criteria analysis model (MapRE) we analyze the cost of various renewable energy generation technologies based on resource availability and key siting criteria such as proximity to transmission and exclusion from steep slopes, dense settlements or ecologically sensitive areas. We find there is more utility-scale solar potential than previously estimated, which can be developed at lower costs than coal power and with minimal cropland tradeoff. We also find significant potential for decentralized roof-top solar in commercial and residential areas. Even with a conservative land use program that reserves maximum land for agriculture and human settlement, there is more renewable energy capacity than needed to support Bangladeshi growth. This study provides critical and timely information for capacity expansion planning in South Asia and demonstrates the use of geospatial models to support decision-making in data-limited contexts