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This paper develops a model and optimization method for multi-area heat and power production with power transmission and storage. The objective function of the model is to minimize the operating costs of the system. The model can be used both for planning optimal system operation, and for simulating the effects of extended production, transmission and storage capacity. The proposed parametric decomposition method is fast enough to solve problems with a large number of hourly models. The parametric decomposition method works in two phases. First, the problem is decomposed into hourly local energy production models without storages and transmission. Parametric linear programming analysis is applied to these models for determining the optimal marginal operating costs as a function of power production. In the second phase, the optimal marginal cost functions are encoded as a linear transshipment network model including storages and transmission network. The network model is solved using generic sparse linear programming software. The operation of each production plant is determined based on the network solution. The decomposition method was validated by comparing it against an integrated linear programming model. The decomposition method demonstrates good accuracy and solves yearly models up to 30 times faster than the integrated model.

The imperative to reduce emissions to counteract climate change has led to the use of renewables progressively in more areas. Looking at district heating, there is a growing interest in coupling current production systems and carbon-neutral technologies. This paper presents a methodology to support decision making about carbon-neutral technologies for district heating. The process is organized in two stages, the first one aims at optimizing the different carbon-neutral technologies according to an objective function and assess uncertainties and dependencies. In the second stage, the alternatives are evaluated using Stochastic Multicriteria Acceptability Analysis (SMAA), a simulation-based method specifically designed to consider imprecise information. The methodology was applied to a case-study in Torino, Italy, which simulates the city district heating network at a smaller scale, with the aim to explore strategies for replacing gas boiler with more sustainable technologies. According to preference information provided by decision makers, the most preferred alternative resulted in the introduction of a solar heat plant combined with an increase size of daily heat storage. Solar heat can benefit from incentives while reducing operational costs and emissions, maximizing the use of carbon-neutral heat thanks to the storage.

Climate change mitigation policy requires reducing dependence on fossil fuels and transition to low carbon energy production in district heating (DH). We study here inclusion of two kinds of renewable energy to a CHP based DH system in Finland: solar heat and ground source heat. In addition, we apply heat storages to balance the gap between production and fluctuating demand. The optimal operation of the extended systems is determined by a simulation and optimization model to minimize the operating costs. We evaluate the different possible extensions in terms of multiple economic, technical and environmental criteria using Stochastic Multicriteria Acceptability Analysis (SMAA). The results show that under Finnish conditions, ground source heat is more favourable than solar heat for DH.

The future energy system in community level should be more ‘smart’ to secure reliability, enhance market service, minimize environmental impact, reduce costs and improve the use of renewable energy source (RES). Therefore, this paper proposes an energy integration system – smart hybrid renewable energy for communities (SHREC). It considers both thermal (heating and cooling) and electricity market in a large community level and highlight the interactions between them through utilizing RES, combined heat and power (CHP) and energy storages. A planning model based on CHP modelling is developed for the SHREC system. A linear programming (LP) algorithm is developed to optimize the SHREC system in a weekly period and the results are compared with an existing energy optimization software. We also demonstrate the model in a sample SHREC system during three typical weeks with cold, warm and mid-season weather in the year 2011. The results indicate that the developed modelling and optimization method is more efficient and flexible for the smart hybrid renewable energy systems.

Renewable energy source (RES) is playing an increasingly important role to reduce fossil fuels in district heating (DH) and to alleviate the accompanying environmental impact. In this paper, a combined heat and power (CHP) based DH system with RES and energy storage system (ESS) is studied. A modelling and optimization method is developed for planning and operating such CHP-DH systems. The objective of the optimization is to minimize the overall costs of the net acquisition for heat and power in deregulated power market. A planning model consisting of energy balances and constraints for system control and operation is built and an efficient algorithm is developed. We demonstrate the method in a CHP-DH system with a solar thermal plant and a thermal energy storage (TES). Results indicate that the developed method is efficient and flexible for planning and operating CHP-DH systems. To simulate the future situation, we also optimize the same CHP-DH system with a higher share of RES and a bigger TES. Results show that TES is used more intensively in the future with more fluctuating CHP load and a higher share of RES.

To achieve efficient transition towards climate and energy framework targets, improvement in energy efficiency is important. This paper presents a model for long-term multi-area combined heat and power production with heat and power storages, and power transmission between areas. Assuming fixed unit commitment, the model minimizes total production and transmission cost. The model can in principle be solved as a linear programming model. However, energy storages impose dynamic constraints to the model, making the long-term model very large and slow to solve. To speed up solution and to allow larger models to be solved, we develop a novel decomposition method that solves three kinds of smaller sub-models iteratively. The method is validated by comparing it with the integrated linear programming model using realistic demand data generated by a forecasting model. The method produces near-optimal solutions within three iterations. The decomposition method can also solve larger models much faster than the integrated model.

Combined heat and power (CHP) production is a very efficient technique to produce power and heat in an integrated process. In CHP plants, generation of heat and power follows a joint characteristic, which means that production planning of both commodities must be done in coordination. The hourly produced power can be sold to the grid at market price, but heat must be produced to meet the local demand of district heating or heat for specific industrial processes. Typically, the most profitable operation of a CHP system can be planned by using an optimization model. The high efficiency and profitability of CHP plants can be further improved by utilization of energy storage units. Heat storages make it possible to relax the constraint to produce heat each hour to exactly match the local demand. This allows satisfying the variable heat demand more cheaply by storing heat during low demand and discharging heat when demand is high. By relaxing the connection between heat and power production, heat storages also allow producing more electricity to the power market when the spot price is high and reducing the power generation when spot price is low. The aim of this study is to develop a model for optimizing the operation of a CHP plant together with a heat storage. The model is a linear programming (LP) model consisting of hourly models connected together with dynamic storage constraints. The objective is to minimize the production (fuel) costs subtracted by revenue from selling power to the market. The model is demonstrated using modified reallife data of a Finnish city. The results are useful for planning efficient operation of the plant. The model can also be adapted for determining the optimal size of the storage.