The carbon tax is applied to your natural gas bill as a tax on the greenhouse gas emissions associated with burning the fuel. The federal carbon tax is applied to provinces that do not already have their own carbon tax system in place, which includes Ontario, New Brunswick, Saskatchewan and Manitoba. Funds collected from the carbon tax will be used to fund energy savings measures in each province that pays the tax.
It is also important to note that electricity generated for the grid that is provided by fossil fuels like coal and natural gas plants may also pay the carbon tax. This could lead to increased electricity prices depending on the efficiency of the fossil-fuel based generation in the province and what percentage of the province’s overall electricity generation is derived from fossil fuels.
We recently downloaded the IESO’s hourly data that contains the entire province of Ontario’s electricity demand numbers from Jan 1 to July 31, 2019 – the data can be downloaded here. We were originally looking to download the data to analyze the top 5 July 2019 peak event days, but thought it would be interesting to do a whole 2019 data review using our REA platform for data visualization and insights. Below is a general review covering annual patterns, weekday/weekend activity, a review of the current top 5 provincial peak days (all occurring in July), and an interesting cost analysis.
Our previous blog post explained the initial work CoEng did with our Licensed Producer client, Agripharm, making sure the optimal rate structure was utilized for their Ontario facility. It was explained that the wrong rate class could cost up to $250,000 annually, which made it a high priority for 2019 to make sure the “Class A Global Adjustment” rate was used.
The following article explains how this Class A rate will also allow for production to double, while keeping electricity cost increases to only 25%.
The Province of Ontario has mandated that buildings as small as 50,000 square feet publicly post their electricity, gas, and water consumption along with their Energy Star score. CoEng recently completed various EWRB applications for our clients to ensure compliance with the annual July 1st deadline. The program in Ontario allows for buildings to enter their general info and energy/water consumption into the Energy Star Portfolio Manager software, and receive an Energy Star score to identify how the building is performing relative to similar building types. The score is between 0 (worst case) and 100 (best case). A score of 50 indicates average performance, and a score of 75 or more indicates a high performing building.
CoEng had a new client come to us 2-weeks before the reporting deadline to support them with their EWRB application. The client, who was part of a Fortune 500 company, had their information validated and entered by CoEng… they were surprised and concerned to see a low score of 35!
CoEng started a partnership with a rapidly growing and expanding Licensed Producer named Agripharm in mid 2018. They were looking for support in their pursuit to achieve the lowest possible energy cost per unit of production. They enrolled in the REA app, which utilizes a software and services platform to automate the work at hand while ensuring there is constant communication between the client and the service provider. Energy and asset management must be practiced… Continuously!
One of the first things analyzed in REA was the rate structure in which Agripharm was enrolled in. Rate structure refers to how a client is billed for energy use and is a major factor in how much an organization pays for their energy each year. For medium to large electricity consumers in Ontario, we immediately look at the business case for Class A vs. Class B Global Adjustment.
As the Class A Global Adjustment opt-in time quickly approaches in Ontario, it is important to start analyzing your peak setting performance during the May 2018 to Apr 2019 base period. It is essential for organizations to understand the business case of Class A vs. Class B in order to decide whether it makes sense to opt-in to Class A by the June 15th, 2019 IESO deadline.
The business case analysis for global adjustment can be rapidly (and affordably) completed by taking advantage of REA’s advanced Energy Dashboard features. The following explains the approach of determining the global adjustment business cases for your organization.
Energy management is a strategy that must be implemented continuously to achieve successful results in the dynamic nature of buildings. The dynamic components of buildings include items like energy consumption, energy rates, maintenance activities, equipment retrofits, seasonal changes and the flow of information.
With buildings being a dynamic entity, energy management cannot be practiced in static events every few years. Instead, energy management must be implemented on a continuous basis to ensure every utility bill is analyzed, the most cost-effective utility rate structure is being used, and maintenance/retrofit projects are coordinated effectively to ensure project benefits are maximized while risk is minimized.
Tired of losing important equipment documentation? Wasting valuable time searching for long lost documents that should be easily accessible? Relying on other companies to store your project data at a high cost? … REA has the solution for you!
A new feature available in REA’s Equipment List is the ability to save online documents for the valuable equipment in a building, preventing the loss of important data and improving overall information management.
An energy manager’s journey with a client typically starts with an energy management plan to holistically understand the building’s operations, mechanical/electrical/controls equipment, and energy consumption data. During creation of the energy plan, an energy manager will create and analyze an equipment list to better understand all equipment information and work to build a relationship between equipment and energy consumption data.
Independent System Operators and Local Distribution Companies often design conservation programs for equipment without knowledge of the actual quantity, age and size of the installations in a region. Optimal program design can only occur when the exact information is known, so the program can target precisely the clients and equipment that are to be replaced with support from an incentive program.