A novel bidding method for combined heat and power units in district heating systems

We propose a bidding method for the participation of combined heat and power (CHP) units in the day-ahead electricity market. More specifically, we consider a district heating system where heat can be produced by CHP units or heat-only units, e.g., gas or wood chip boilers. We use a mixed-integer linear program to determine the optimal operation of the portfolio of production units and storages on a daily basis. Based on the optimal production of subsets of units, we can derive the bidding prices and amounts of electricity offered by the CHP units for the day-ahead market. The novelty about our approach is that the prices are derived by iteratively replacing the production of heat-only units through CHP production. This results in an algorithm with a robust bidding strategy that does not increase the system costs even if the bids are not won. We analyze our method on a small realistic test case to illustrate our method and compare it with other bidding strategies from literature, which consider CHP units individually. The analysis shows that considering a portfolio of units in a district heating system and determining bids based on replacement of heat production of other units leads to better results

Operational planning and bidding for district heating systems with uncertain renewable energy production

In countries with an extended use of district heating (DH), the integrated operation of DH and power systems can increase the flexibility of the power system achieving a higher integration of renewable energy sources (RES). DH operators can not only provide flexibility to the power system by acting on the electricity market, but also profit from the situation to lower the overall system cost. However, the operational planning and bidding includes several uncertain components at the time of planning: electricity prices as well as heat and power production from RES. In this publication, we propose a planning method that supports DH operators by scheduling the production and creating bids for the day-ahead and balancing electricity markets. The method is based on stochastic programming and extends bidding strategies for virtual power plants to the DH application. The uncertain factors are considered explicitly through scenario generation. We apply our solution approach to a real case study in Denmark and perform an extensive analysis of the production and trading behaviour of the DH system. The analysis provides insights on how DH system can provide regulating power as well as the impact of uncertainties and renewable sources on the planning. Furthermore, the case study shows the benefit in terms of cost reductions from considering a portfolio of units and both markets to adapt to RES production and market states

Commitment and Dispatch of Heat and Power Units via Affinely Adjustable Robust Optimization

The joint management of heat and power systems is believed to be key to the integration of renewables into energy systems with a large penetration of district heating. Determining the day-ahead unit commitment and production schedules for these systems is an optimization problem subject to uncertainty stemming from the unpredictability of demand and prices for heat and electricity. Furthermore, owing to the dynamic features of production and heat storage units as well as to the length and granularity of the optimization horizon (e.g., one whole day with hourly resolution), this problem is in essence a multi-stage one. We propose a formulation based on robust optimization where recourse decisions are approximated as linear or piecewise-linear functions of the uncertain parameters. This approach allows for a rigorous modeling of the uncertainty in multi-stage decision-making without compromising computational tractability. We perform an extensive numerical study based on data from the Copenhagen area in Denmark, which highlights important features of the proposed model. Firstly, we illustrate commitment and dispatch choices that increase conservativeness in the robust optimization approach. Secondly, we appraise the gain obtained by switching from linear to piecewise-linear decision rules within robust optimization. Furthermore, we give directions for selecting the parameters defining the uncertainty set (size, budget) and assess the resulting trade-off between average profit and conservativeness of the solution. Finally, we perform a thorough comparison with competing models based on deterministic optimization and stochastic programming.Comment: 31 page

Optimal operation of combined heat and power systems: an optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

An Economic, Energy, and Environmental Analysis of PV/Micro-CHP Hybrid Systems: A Case Study of a Tertiary Building

Our present standard of living depends strongly on energy sources, with buildings being a primary focus when it comes to reducing energy consumption due to their large contribution, especially in tertiary buildings. The goal of the present study is to evaluate the performance of two different designs of hybrid systems, composed of natural gas engines and photovoltaic panels. This will be done through simulations in TRNSYS, considering a representative office building with various schedules of operation (8, 12, and 24 h), as well as different climates in Spain. The main contributions of this paper are the evaluations of primary energy-consumption, emissions, and economic analyses for each scenario. In addition, a sensitivity analysis is carried out to observe the influence of energy prices, as well as that of the costs of the micro-CHP engines and PV modules. The results show that the scenario with the conventional system and PV modules is the most profitable one currently. However, if electricity prices are increased in the future or natural gas prices are reduced, the scenario with micro-CHP engines and PV modules will become the most profitable option. Energy service engineers, regulators, and manufacturers are the most interested in these results

Operation of distributed generation under stochastic prices

The ongoing deregulation of electricity industries worldwide is providing incentives for microgrids, entities that use small-scale distributed generation (DG) and combined heat and power (CHP) ap- plications to meet local energy loads, to evolve independently of the traditional centralised grid in order to provide greater flexibility and energy efficiency to end-use consumers. We examine the impact of start-up costs on the option values and operating schedules of on-site DG installed by a microgrid in the presence of stochastic electricity and fuel prices. We proceed by formulating a stochastic dynamic programme (SDP) for the microgrid that minimises its expected discounted cost over a time horizon and solving it using least-squares Monte Carlo (LSMC) simulation. The expected cost saving that the microgrid realises by having gas-fired DG installed relative to meeting its entire electric load via off-site purchases is the implied option value of DG. Numerical examples indicate that although start-up costs do not significantly lower DG value, they, nevertheless, have a profound impact on the optimal DG operating schedule as the microgrid must incorporate not only current, but also future, expected start-up costs into its current decision-making process as an opportunity cost. As a consequence, the microgrid becomes more hesitant to turn DG units on (off), preferring to wait until the electricity price (natural gas generating cost) exceeds the natural gas generating cost (electricity price) by a significant margin before taking action. We demonstrate that ignoring this tradeoff and proceeding myopically as in the case without start-up costs results in drastically higher expected costs and fewer opportunities to use DG

Optimized Household Demand Management with Local Solar PV Generation

Demand Side Management (DSM) strategies are of-ten associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side manage-ment is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum so-lution to the problem of scheduling of household demand side management in the presence of PV generation under a set of tech-nical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid