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PV Assets Using The Optimization Factor Methodology

 

Mo Rousso (Founder and COO, HelioPower) has written this article about HelioPower managing Photovoltaic (PV) Assets using the Optimization Factor Methodology.

PV Assets and the Optimization Factor Methodology

Photovoltaic (PV) systems are long-lived assets with unique lifecycle management needs.  As such, they require special operations and maintenance processes to ensure reliable and profitable operation over their life span.  As an owner and operator of over 120 PV systems, as well as providing asset management for a number of client portfolios, HelioPower is in a unique position to design and implement a methodology that provides us with our desired asset management outcomes.

To us, the overarching factor in successful asset management (AM) is to initially define the Optimization Factor (OF) as the first step of the methodology.  The OF is defined as the single factor around which the AM program is optimized and drives the overall architecture of the AM practice.  OF’s may include LCOE, reliability, production, risk, or cost and may be different for different asset owners.

Once the OF is determined, HelioPower then employs a process to structure the AM architecture.  The key AM architecture elements include:

  • Operational processes – these include the day-to-day operation of each solar plant and the ability to dispatch resources to meet the operational need
  • Maintenance regime – this includes defining the Hierarchy of Failure (HoF) and the balance between planned and corrective maintenance
  • Optimization parameters – this refers to asset owner’s overarching goal for these assets. Is it to maximize LCOE, IRR, reliability, production, or some other factor?
  • Data resource management –defining the data elements and structure, and development and execution of architectures, practices and procedures to manage the full data lifecycle and meet the asset owner’s decision management and application interface needs.
  • Business Intelligence– determines the evidence-based asset decision-making process and the knowledge needed to make better decisions. These reports culminate in defined actions that reflect the data-driven insight bridging the gap between knowledge and action.
  • Solution Integration– application integration and data resource management make possible intelligent the decision- making and reporting and is the key to reducing operational-decision making risks. Data resources must be shared between applications required to successfully fulfill the asset management process.  Information must be shared between key functional application modules such as CMMS (job costing, work-orders), Dispatching, AM modules, operations and finance applications, and SCADA interface to
  • provide consistent decisions and intelligent actions

The following figure represents the typical approach used to determine the Optimization Factor to best succeed in asset management.

When HelioPower brings in a new asset to be managed, or is hired by an asset owner to improve their AM practice, we use a team with significant asset management experience and specifically who have expertise and experience within the context of business process engineering, software integration, data architecture, O&M practices and technologies, and energy analytics.  In addition, our team has significant experience in “Business Insight” and “Evidence-Based Decision Making.”  We believe these are the essential elements to shape the path of our client’s asset management implementation success.  Understanding how predictive analysis and data resource management can improve AM performance while reducing risk is paramount… and hence provides insight.  Ensuring data governance throughout all applications and functions requires data modeling concepts supported by functional integration… providing evidence-based decision making.

To summarize, as shown in the graphic above, the approach is executed in the following steps:

  1. Define the OF and strategy
  2. Understand the Current Situation
  3. Based on the asset owner’s goals versus where they are today, assess their needs and conduct a gap analysis
  4. Finally, develop and present recommendations in an actionable plan

Project Approach

Life Cycle CostsHelioPower’s goal in effectively managing a PV asset, either owned or managed by us, is to optimize around LCOE to maximize reliability while minimizing lifecycle costs.

Dispatch ProcessWe achieve our optimization goal through the use of our unique Hierarchy of Failure (HoF) analysis. HoF is the synthesis of:

  • Resources — such as automated systems, technicians, manufacturer support, spare parts, and special tools.
  • Needs – component criticality to achieve the OF.
  • Actions – the standard menu of responses to handle 80% of all maintenance requirements All of these parameters must be considered in order to provide an effective and efficient response. Basically, we determine:
  • The criticality of the component to achieve the OF.
  • The availability of the replacement component.
  • The availability of the technician and any special tools required.
  • The uniqueness of the dispatch and repair process for that particular component.

This process is repeated for every component that is identified within the HoF.  Then components are sorted and ranked to determine the overall hierarchy and to drive the requirements of the NOC architecture.

The architecture is composed of the Computerized Maintenance Management System (CMMS), the underlying data structure, and the interface to other organizational systems, such as accounting and project management.

The requirements of the CMMS are directly driven from the maintenance regime created to manage maintenance costs while ensuring reliability.

We design our maintenance management programs around the simple philosophy that planned maintenance is cheaper than unplanned maintenance.

MM

Planned Maintenance is that done in advance of component failure and is comprised of:

  • Predictive Maintenance – work orders driven in anticipation of a failure and based on trend lines. These may include module washings based on production output trends or inverter board swaps based on capacitor temperature.
  • Preventative Maintenance – work orders driven from standard intervals. These may include panel washings based on calendar weeks or inverter filter changes based on manufacturer recommendations for operating hours.

Corrective Maintenance is that done in reaction to a component failure or an alarm condition; such as an inverter board failure, module diode failure, or a circuit breaker/fuse blow.  This is typically the most expensive type of failure since it usually incurs lengthy unplanned outages, expedited shipping, and after-hour technician calls.  However, there may be cases where RTF (run to failure) is acceptable.  This usually involves non-critical equipment, inexpensive spare parts on-hand, or ease of repair.  In many cases it is the combination of these factors.  A good example of where RTF applies in a PV plant would be the weather station.

To summarize, the Optimization Factor driven asset management program:

  1. Begins with the end in mind and establishes the OF as the guiding principle for defining the AM structure
  2. Determines the current situation and develops an AM Roadmap
  3. Defines the HoF that includes PV plant components integral to the AM program
  4. Develops the NOC architecture best suited to achieve the program goals

Through the use of our OF driven AM practice, HelioPower expects to enjoy a significant increase in both production and reliability and a decrease in maintenance costs and unscheduled downtime, meaning that our AM program will be delivering an optimized LCOE to both our portfolio and those of our clients.